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modelId
string | author
string | last_modified
unknown | downloads
int64 | likes
int64 | library_name
string | tags
sequence | pipeline_tag
string | createdAt
unknown | card
string |
|---|---|---|---|---|---|---|---|---|---|
hravi/results | hravi | "2025-05-09T16:52:15" | 4 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2025-03-31T03:38:20" | ---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: results
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2723
- Accuracy: 0.9513
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5075 | 1.0 | 1907 | 0.3479 | 0.9332 |
| 0.2367 | 2.0 | 3814 | 0.2869 | 0.9497 |
| 0.1933 | 3.0 | 5721 | 0.2723 | 0.9513 |
### Framework versions
- Transformers 4.50.3
- Pytorch 2.6.0
- Datasets 3.5.0
- Tokenizers 0.21.1
|
vegeta03/DeepHermes-Financial-Fundamentals-Prediction-Specialist-Atropos-Q8_0-GGUF | vegeta03 | "2025-05-09T16:18:54" | 0 | 0 | transformers | [
"transformers",
"gguf",
"Llama-3",
"Financial Analysis",
"RL",
"Atropos",
"Fundamentals Prediction",
"Nous Research",
"reasoning",
"reinforcement learning",
"json mode",
"llama-cpp",
"gguf-my-repo",
"en",
"base_model:NousResearch/DeepHermes-Financial-Fundamentals-Prediction-Specialist-Atropos",
"base_model:quantized:NousResearch/DeepHermes-Financial-Fundamentals-Prediction-Specialist-Atropos",
"license:llama3",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-05-09T16:18:19" | ---
base_model: NousResearch/DeepHermes-Financial-Fundamentals-Prediction-Specialist-Atropos
language:
- en
library_name: transformers
license: llama3
tags:
- Llama-3
- Financial Analysis
- RL
- Atropos
- Fundamentals Prediction
- Nous Research
- reasoning
- transformers
- reinforcement learning
- json mode
- llama-cpp
- gguf-my-repo
---
# vegeta03/DeepHermes-Financial-Fundamentals-Prediction-Specialist-Atropos-Q8_0-GGUF
This model was converted to GGUF format from [`NousResearch/DeepHermes-Financial-Fundamentals-Prediction-Specialist-Atropos`](https://huggingface.co/NousResearch/DeepHermes-Financial-Fundamentals-Prediction-Specialist-Atropos) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/NousResearch/DeepHermes-Financial-Fundamentals-Prediction-Specialist-Atropos) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo vegeta03/DeepHermes-Financial-Fundamentals-Prediction-Specialist-Atropos-Q8_0-GGUF --hf-file deephermes-financial-fundamentals-prediction-specialist-atropos-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo vegeta03/DeepHermes-Financial-Fundamentals-Prediction-Specialist-Atropos-Q8_0-GGUF --hf-file deephermes-financial-fundamentals-prediction-specialist-atropos-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo vegeta03/DeepHermes-Financial-Fundamentals-Prediction-Specialist-Atropos-Q8_0-GGUF --hf-file deephermes-financial-fundamentals-prediction-specialist-atropos-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo vegeta03/DeepHermes-Financial-Fundamentals-Prediction-Specialist-Atropos-Q8_0-GGUF --hf-file deephermes-financial-fundamentals-prediction-specialist-atropos-q8_0.gguf -c 2048
```
|
ruanchengren/Qwen2.5-72B-Instruct-bnb-4bit-Gensyn-Swarm-prehistoric_scavenging_barracuda | ruanchengren | "2025-05-09T16:07:26" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am prehistoric scavenging barracuda",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-72B-Instruct-bnb-4bit",
"base_model:finetune:Gensyn/Qwen2.5-72B-Instruct-bnb-4bit",
"endpoints_compatible",
"region:us"
] | null | "2025-05-08T19:22:03" | ---
base_model: Gensyn/Qwen2.5-72B-Instruct-bnb-4bit
library_name: transformers
model_name: Qwen2.5-72B-Instruct-bnb-4bit-Gensyn-Swarm-prehistoric_scavenging_barracuda
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am prehistoric scavenging barracuda
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-72B-Instruct-bnb-4bit-Gensyn-Swarm-prehistoric_scavenging_barracuda
This model is a fine-tuned version of [Gensyn/Qwen2.5-72B-Instruct-bnb-4bit](https://huggingface.co/Gensyn/Qwen2.5-72B-Instruct-bnb-4bit).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="ruanchengren/Qwen2.5-72B-Instruct-bnb-4bit-Gensyn-Swarm-prehistoric_scavenging_barracuda", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Grogros/Llama-3.2-1B-Instruct-distillation-SecretSauce-3.0-AlpacaRefuseSmooth-sauce2lrLong | Grogros | "2025-05-09T15:32:00" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-1B-Instruct",
"license:llama3.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-05-09T09:36:00" | ---
library_name: transformers
license: llama3.2
base_model: meta-llama/Llama-3.2-1B-Instruct
tags:
- generated_from_trainer
model-index:
- name: Llama-3.2-1B-Instruct-distillation-SecretSauce-3.0-AlpacaRefuseSmooth-sauce2lrLong
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Llama-3.2-1B-Instruct-distillation-SecretSauce-3.0-AlpacaRefuseSmooth-sauce2lrLong
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- optimizer: Use adafactor and the args are:
No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 4000
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.2.0a0+81ea7a4
- Datasets 3.5.0
- Tokenizers 0.21.1
|
flyingbugs/Qwen2.5-Math-7B-OpenR1-Math-220k-pruned-think-mid | flyingbugs | "2025-05-09T15:22:08" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"sft",
"conversational",
"dataset:flyingbugs/OpenR1-Math-220k-pruned-think_mid",
"base_model:flyingbugs/Qwen2.5-Math-7B-Instruct",
"base_model:finetune:flyingbugs/Qwen2.5-Math-7B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-05-08T20:55:36" | ---
base_model: flyingbugs/Qwen2.5-Math-7B-Instruct
datasets: flyingbugs/OpenR1-Math-220k-pruned-think_mid
library_name: transformers
model_name: Qwen2.5-Math-7B-OpenR1-Math-220k-pruned-think-mid
tags:
- generated_from_trainer
- open-r1
- trl
- sft
licence: license
---
# Model Card for Qwen2.5-Math-7B-OpenR1-Math-220k-pruned-think-mid
This model is a fine-tuned version of [flyingbugs/Qwen2.5-Math-7B-Instruct](https://huggingface.co/flyingbugs/Qwen2.5-Math-7B-Instruct) on the [flyingbugs/OpenR1-Math-220k-pruned-think_mid](https://huggingface.co/datasets/flyingbugs/OpenR1-Math-220k-pruned-think_mid) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="flyingbugs/Qwen2.5-Math-7B-OpenR1-Math-220k-pruned-think-mid", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/jjh233/huggingface/runs/le6ol498)
This model was trained with SFT.
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Sugyeong/mistral_moce_inst_c4_new | Sugyeong | "2025-05-09T14:28:09" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-05-09T14:23:01" | <!DOCTYPE html>
<html class="" lang="en">
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<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
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const key = "_tb_global_settings";
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: "light";
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delfincarlos82/cardel | delfincarlos82 | "2025-05-09T14:26:53" | 0 | 0 | null | [
"region:us"
] | null | "2025-05-09T14:26:53" | <!DOCTYPE html>
<html class="" lang="en">
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name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
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Noto Color Emoji;
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margin: 0 auto 1rem;
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font-size: 3.75rem;
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ksngi56/gensyn-checkpoints-ravenous_bellowing_raccoon | ksngi56 | "2025-05-09T14:25:41" | 8 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am ravenous bellowing raccoon",
"unsloth",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-21T02:47:59" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
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alt=""
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<h1>429</h1>
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</html> |
ldostadi/gemma-3-4b-it-abliterated-Q5_K_M-GGUF | ldostadi | "2025-05-09T14:24:42" | 0 | 0 | transformers | [
"transformers",
"gguf",
"abliterated",
"uncensored",
"llama-cpp",
"gguf-my-repo",
"image-text-to-text",
"base_model:huihui-ai/gemma-3-4b-it-abliterated",
"base_model:quantized:huihui-ai/gemma-3-4b-it-abliterated",
"license:gemma",
"endpoints_compatible",
"region:us",
"conversational"
] | image-text-to-text | "2025-05-09T14:24:26" | <!DOCTYPE html>
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Noto Color Emoji;
}
img {
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margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
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margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
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.dark main {
background-color: rgb(11, 15, 25);
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.dark p, .dark a {
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}
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sinobaba/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-bellowing_muscular_ostrich | sinobaba | "2025-05-09T14:16:51" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am bellowing muscular ostrich",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | "2025-05-09T12:44:26" | <!DOCTYPE html>
<html class="" lang="en">
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<meta
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BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
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New-Tutorial-Shah-Sapna-Viral-Video/Original.Viral.Clip.Sapna.Shah.Viral.Video.Leaks.Official | New-Tutorial-Shah-Sapna-Viral-Video | "2025-05-09T14:03:44" | 0 | 0 | null | [
"region:us"
] | null | "2025-05-09T14:01:52" | <!DOCTYPE html>
<html class="" lang="en">
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img {
width: 6rem;
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margin: 0 auto 1rem;
}
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box-sizing: border-box;
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color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
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</style>
<script>
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chau003/yolo | chau003 | "2025-05-09T13:53:14" | 0 | 0 | null | [
"region:us"
] | null | "2025-05-09T13:53:06" | <!DOCTYPE html>
<html class="" lang="en">
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<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
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sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
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alt=""
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<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p>
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MinHyeong/pythia_margin | MinHyeong | "2025-05-09T13:47:41" | 0 | 0 | null | [
"safetensors",
"gpt_neox",
"region:us"
] | null | "2025-05-09T13:35:48" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
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/>
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margin: 0;
}
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text-align: center;
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BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
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if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
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<img
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alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p>
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imdatta0/qwen4b_dmath_matheval_unsloth | imdatta0 | "2025-05-09T13:42:20" | 0 | 0 | null | [
"region:us"
] | null | "2025-05-09T13:42:20" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
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/>
<meta property="fb:app_id" content="1321688464574422" />
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<style>
body {
margin: 0;
}
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background-color: white;
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padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
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if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
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alt=""
/>
<div>
<h1>429</h1>
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nikojim/Llama-3.1-8B-bnb-4bit-test | nikojim | "2025-05-09T13:42:15" | 0 | 0 | null | [
"region:us"
] | null | "2025-05-09T13:42:14" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
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margin: 0;
}
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text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
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alt=""
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<h1>429</h1>
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numerouno01/c1aa4a82-dffb-422e-b0bf-1c6673ab1e15 | numerouno01 | "2025-05-09T13:39:42" | 0 | 0 | null | [
"safetensors",
"gpt_neox",
"region:us"
] | null | "2025-05-09T13:26:17" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
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margin: 0;
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background-color: white;
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text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
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kmpartner/bkv2tpcmlra-test | kmpartner | "2025-05-09T13:28:49" | 273 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"arxiv:1910.09700",
"base_model:nota-ai/bk-sdm-v2-tiny",
"base_model:adapter:nota-ai/bk-sdm-v2-tiny",
"region:us"
] | null | "2025-04-13T07:00:20" | <!DOCTYPE html>
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width: 6rem;
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margin: 0 auto 1rem;
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kostiantynk1205/46317ee6-4e03-4058-b824-de1914b69c04 | kostiantynk1205 | "2025-05-09T13:27:06" | 0 | 0 | peft | [
"peft",
"generated_from_trainer",
"base_model:EleutherAI/pythia-70m",
"base_model:adapter:EleutherAI/pythia-70m",
"region:us"
] | null | "2025-05-09T13:26:58" | <!DOCTYPE html>
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img {
width: 6rem;
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margin: 0 auto 1rem;
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h1 {
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line-height: 1.75rem;
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box-sizing: border-box;
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}
.dark p, .dark a {
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</style>
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bigband/ProsperousKali | bigband | "2025-05-09T13:20:56" | 0 | 0 | null | [
"safetensors",
"gemma3",
"region:us"
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img {
width: 6rem;
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margin: 0 auto 1rem;
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h1 {
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color: rgba(31, 41, 55, 1);
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color: rgba(107, 114, 128, 1);
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line-height: 1.75rem;
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color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
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Minasanjotaro/81db10a4-8c17-4d95-887d-02d3647eb46a | Minasanjotaro | "2025-05-09T13:18:31" | 0 | 0 | null | [
"region:us"
] | null | "2025-05-09T12:57:17" | <!DOCTYPE html>
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img {
width: 6rem;
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margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
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color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
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p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
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.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
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Negan1exe/gpu.world | Negan1exe | "2025-05-09T13:15:34" | 0 | 0 | null | [
"region:us"
] | null | "2025-05-09T13:15:33" | <!DOCTYPE html>
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Noto Color Emoji;
}
img {
width: 6rem;
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margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
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font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
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John6666/ars-caelestis-726a-v1alpha-sdxl | John6666 | "2025-05-09T13:13:33" | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"anime",
"girls",
"merge",
"pony",
"noobai",
"illustrious",
"en",
"base_model:Laxhar/noobai-XL-1.0",
"base_model:merge:Laxhar/noobai-XL-1.0",
"base_model:NeverWinter13/Ars-Divina-v1.1",
"base_model:merge:NeverWinter13/Ars-Divina-v1.1",
"base_model:OnomaAIResearch/Illustrious-xl-early-release-v0",
"base_model:merge:OnomaAIResearch/Illustrious-xl-early-release-v0",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | "2025-05-09T13:05:12" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
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BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
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if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
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goodenwings/7fc4245d-ac5d-4360-8498-b483beb9f135 | goodenwings | "2025-05-09T13:11:48" | 0 | 0 | null | [
"region:us"
] | null | "2025-05-09T13:11:26" | <!DOCTYPE html>
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BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
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if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
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dimasik2987/03b47024-a4ca-4215-83d2-9be643a79954 | dimasik2987 | "2025-05-09T13:10:31" | 0 | 0 | null | [
"region:us"
] | null | "2025-05-09T11:32:24" | <!DOCTYPE html>
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KenAzr/kenan-finetuned-bert2_next | KenAzr | "2025-05-09T13:01:51" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2025-05-09T12:36:34" | <!DOCTYPE html>
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vmpsergio/9aa9a549-8df7-4462-b01f-e257c9c84fcf | vmpsergio | "2025-05-09T12:58:19" | 0 | 0 | null | [
"region:us"
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llmismylife/AI-Tuned-DeepSeek-R1-Distill-Qwen-1.5B | llmismylife | "2025-05-09T12:56:56" | 12 | 0 | null | [
"safetensors",
"qwen3",
"region:us"
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sadaisystems/sadai-mrec-qwen2.5-3B-v0.0.1 | sadaisystems | "2025-05-09T12:56:31" | 153 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"dataset:sadaisystems/sadai-mrec-query-rewrite-13k",
"base_model:Qwen/Qwen2.5-3B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-3B-Instruct",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-05-07T15:03:16" | <!DOCTYPE html>
<html class="" lang="en">
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Jazhyc/trocr-base-line-patches | Jazhyc | "2025-05-09T12:55:17" | 0 | 0 | null | [
"safetensors",
"vision-encoder-decoder",
"base_model:microsoft/trocr-base-stage1",
"base_model:finetune:microsoft/trocr-base-stage1",
"license:mit",
"region:us"
] | null | "2025-05-09T12:48:59" | <!DOCTYPE html>
<html class="" lang="en">
<head>
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mjs227/rltu_sft_0_6-qwen-merged | mjs227 | "2025-05-09T12:53:19" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-05-09T12:45:22" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
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Noto Color Emoji;
}
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width: 6rem;
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}
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sharkMeow/train_V2_CLIP | sharkMeow | "2025-05-09T12:43:12" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"chinese_clip",
"generated_from_trainer",
"base_model:OFA-Sys/chinese-clip-vit-base-patch16",
"base_model:finetune:OFA-Sys/chinese-clip-vit-base-patch16",
"endpoints_compatible",
"region:us"
] | null | "2025-05-08T08:37:29" | <!DOCTYPE html>
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Noto Color Emoji;
}
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width: 6rem;
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margin: 0 auto 1rem;
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font-size: 3.75rem;
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kueltzho/mistral-small-3.1-instruct-2503-trl-sft-ChartQA | kueltzho | "2025-05-09T12:41:45" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:mistralai/Mistral-Small-3.1-24B-Instruct-2503",
"base_model:finetune:mistralai/Mistral-Small-3.1-24B-Instruct-2503",
"endpoints_compatible",
"region:us"
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Beless/gibvllm | Beless | "2025-05-09T12:35:55" | 0 | 0 | null | [
"region:us"
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juhx/q200 | juhx | "2025-05-09T12:34:43" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-05-09T12:29:52" | <!DOCTYPE html>
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BabyLM-community/babylm-baseline-10m-gpt-bert-masked-focus | BabyLM-community | "2025-05-09T12:20:35" | 78 | 0 | null | [
"pytorch",
"babylm-baseline",
"strict-small",
"babylm-2025",
"custom_code",
"en",
"arxiv:2502.10645",
"arxiv:2405.09605",
"arxiv:2411.07990",
"license:apache-2.0",
"region:us"
] | null | "2025-05-01T19:58:38" | ---
license: apache-2.0
language:
- en
tags:
- babylm-baseline
- strict-small
- babylm-2025
---
# Model Card for GPT-BERT Masked Focus Small
<!-- Provide a quick summary of what the model is/does. [Optional] -->
A 31M model trained on 100M (10M unique words) able to do both causal and masked inference.
# Table of Contents
- [Model Card for GPT-BERT Small Causal Focus](#model-card-for--model_id-)
- [Table of Contents](#table-of-contents)
- [Model Details](#model-details)
- [Model Description](#model-description)
- [Uses](#uses)
- [Training Details](#training-details)
- [Training Data](#training-data)
- [Hyperparameters](#hyperparameters)
- [Training Procedure](#training-procedure)
- [Size and Checkpoints](#size-and-checkpoints)
- [Evaluation](#evaluation)
- [Testing Data & Metrics](#testing-data-factors--metrics)
- [Testing Data](#testing-data)
- [Metrics](#metrics)
- [Hyperparameters](#hyperparameters)
- [Results](#results)
- [Technical Specifications](#technical-specifications-optional)
- [Model Architecture and Objective](#model-architecture-and-objective)
- [Compute Infrastructure](#compute-infrastructure)
- [Hardware](#hardware)
- [Software](#software)
- [Training Time](#training-time)
- [Citation](#citation)
- [Model Card Authors](#model-card-authors-optional)
- [Bibliography](#bibliography)
# Model Details
## Model Description
<!-- Provide a longer summary of what this model is/does. -->
This one of the three GPT-BERT baselines for the strict-small track of the 2025 BabyLM challenge.
This specific model is trained with a majority number of examples being masked and a minority being masked.
- **Developed by:** Lucas Georges Gabriel Charpentier
- **Model type:** Language model (Causal and Masked)
- **Language(s) (NLP):** eng
- **License:** apache-2.0
- **Resources for more information:**
- [GitHub Repo](https://github.com/ltgoslo/gpt-bert)
# Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
This is a pre-trained language model.
It can be used to evaluate tasks zero-shot in both a causal and masked setting.
It can also be fine-tuned by adding a new head and dropping the language modeling head.
It can be used for language generation but given its small size and low number of words trained on, do not expect LLM-level performance.
It can also be used for mask infilling.
# Training Details
## Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
We used the BabyLM 10M (Strict-small) dataset to train the model. It is composed in the following way:
| Source | Weight | Domain | Citation | Website | License |
| --- | --- | --- | --- | --- | --- |
| BNC | 8% | Dialogue | BNC Consortium (2007) | [link](http://www.natcorp.ox.ac.uk/) | [link](http://www.natcorp.ox.ac.uk/docs/licence.html) <sup>1</sup> |
| CHILDES | 29% | Dialogue, Child-Directed | MacWhinney (2000) | | [link](https://talkbank.org/share/rules.html) |
| Project Gutenberg | 26% | Fiction, Nonfiction | Gerlach & Font-Clos (2020) | [link](https://github.com/pgcorpus/gutenberg) | [link](https://www.gutenberg.org/policy/license.html) |
| OpenSubtitles | 20% | Dialogue, Scripted | Lison & Tiedermann (2016) | [link](https://opus.nlpl.eu/OpenSubtitles-v2018.php) | Open source |
| Simple English Wikipedia | 15% | Nonfiction | -- | [link](https://dumps.wikimedia.org/simplewiki/20221201/) | [link](https://dumps.wikimedia.org/legal.html) |
| Switchboard | 1% | Dialogue | Godfrey et al. (1992), Stolcke et al., (2000) | [link](http://compprag.christopherpotts.net/swda.html) | [link](http://compprag.christopherpotts.net/swda.html) |
<sup>1</sup> Our distribution of part of the BNC Texts is permitted under the fair dealings provision of copyright law (see term (2g) in the BNC license).
## Hyperparameters
| Hyperparameter | Value |
| --- | --- |
| % Causal Objective | 6.25% |
| % Masked Objective | 93.75% |
| Sequence Length | 128 → 512 |
| Batch Size (in tokens) | 16 384 |
| Learning Rate | 0.007 |
| Number of Steps | 9 914 |
| Warmup Ratio | 1.6% |
| Cooldown Ratio | 1.6% |
| Mask Ratio | 0.3 → 0.15 |
| Random Ratio | 0.1 |
| Keep Ratio | 0.1 |
| Weight Decay | 0.1 |
| Optimizer | LAMB |
| Optimizer Epsilon | 10<sup>-8</sup> |
| Optimizer Beta_1 | 0.9 |
| Optimizer Beta_2 | 0.98 |
| Grdient Clipping | 2.0 |
| Z-Loss weight | 0.0001 |
## Training Procedure
During training we vary both the mask token percentage (linear decay from 30% to 15%), and the sequence length.
For the sequence length we make sure to keep the total tokens per batch the same by reducing the batch size proportionally to the sequence length.
We have three steps in the sequence length:
- We start with a sequence length of 128 for 60% of the training.
- The next 20% has a sequence length of 256.
- The final 20% has a sequence length of 512.
We use a Warmup-Cosine-Cooldown scheduler for the training with the percentages reported in the [Hyperparameters](#hyperparameters)
### Size and checkpoints
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
The model has 31M parameters.
In total we train on around 100M words (or ten repetitions of the training set).
We provide multiple checkpoints from the training.
Specifically we provode:
- Checkpoints every 1M words of pretraining for the first 10M words (or every 99.14 steps)
- Checkpoints every 10M words of pretraining for the first 100M words (or every 991.4 steps)
# Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
This model is evaluated in three different fashions:
1. We provide a validation loss calculated on 1M words from the development set of the BabyLM data (same source as those found in [Training Data](#training-data)).
2. We do zero-shot evaluation on 7 tasks.
3. We do fine-tuning on a subset of the (Super)GLUE tasks (Wang et al., ICLR 2019; Wang et al., NeurIPS 2019) .
## Testing Data & Metrics
### Testing Data
<!-- This should link to a Data Card if possible. -->
For the BLiMP, BLiMP supplement, and EWoK tasks, we use a filtered version of the dataset to only include examples with words found in the BabyLM dataset.
For the Finetuning task, we both filter and sample down to a maximum 10 000 train examples.
*Validation Data*
1M words from the developement split of BabyLM.
The evaluation is done using the Masked Next Token Prediction objective.
*Zero-shot Tasks*
- **BLiMP**: The Benchmark of Linguistic Minimal Pairs evaluates the model's linguistic ability by seeing if it can recognize the grammatically correct sentence from a pair of minimally different sentences. It tests various grammatical phenomena.(Warstadt et al., TACL 2020)
- **BLiMP Supplement**: A supplement to BLiMP introduced in the first edition of the BabyLM challenge. More focused on dialogue and questions. (Warstadt et al., CoNLL-BabyLM 2023)
- **EWoK**: Works similarly to BLiMP but looks the model's internal world knowledge. Looking at both whter a model has physical and social knowledge. (Ivanova et al., 2024)
- **Eye Tracking and Self-paced Reading**: Looks at whether the model can mimick the eye tracking and reading time of a human but using surprisal of a word as a proxy for time spent reading a word. (de Varda et al., BRM 2024)
- **Entity Tracking**: Checks whether a model can keep track of the changes to the states of entities as text/dialogue unfolds. (Kim & Schuster, ACL 2023)
- **WUGs**: Tests morphological generalization in LMs through an adjective nominalization task. (Hofmann et al., 2024)
*Finetuning Tasks*
- **BoolQ**: A yes/no QA dataset with unprompted and unconstrained questions. (Clark et al., NAACL 2019)
- **MNLI**: The Multi-Genre Natural Language Inference corpus tests the language understanding of a model by seeing wehther it can recognize textual entailment. (Williams et al., NAACL 2018)
- **MRPC**: The Microsoft Research Paraphrase Corpus contains pairs of sentences that are either paraphrases/semntically equivalent to each other or unrelated.(Dolan & Brockett, IJCNLP 2005)
- **QQP**<sup>2</sup>: Similarly to MRPC, the Quora Question Pairs corpus tests the models ability to determine whether a pair of questions are sematically similar to each other. These questions are sourced from Quora.
- **MultiRC**: The Multi-Sentence Reading Comprehension corpus is a QA task that evaluates the model's ability to the correct answer from a list of answers given a question and context paragraph. In this version the data is changed to a binary classification judging whether the answer to a question, context pair is correct. (Khashabi et al., NAACL 2018)
- **RTE**: Similar the Recognizing Text Entailement tests the model's ability to recognize text entailement. (Dagan et al., Springer 2006; Bar et al., 2006; Giampiccolo et al., 2007; Bentivogli et al., TAC 2009)
- **WSC**: The Winograd Schema Challenge tests the models ability to do coreference resolution on sentences with a pronoun and a list of noun phrases found in the sentence. This version edits it to be a binary classification on examples consisting of a pronoun and noun phrase.(Levesque et al., PKRR 2012)
<sup>2</sup> https://www.quora.com/profile/Ricky-Riche-2/First-Quora-Dataset-Release-Question-Pairs
### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
The metrics used to evaluate the model are the following:
- Validation Data
- Cross-entropy loss on the masked tokens
- Zero-shot
- Accuracy on predicting the correct completion/sentence for BLiMP, BLiMP Supplement, EWoK, Entity Tracking, and WUGs
- Change in R^2 prediction from baseline for Eye Tracking (with no spillover) and Self-paced Reading (1-word spillover)
- Finetuning
- 3 class Accuracy for MNLI
- Binary Accuracy for BoolQ, MultiRC, and WSC
- F1-score for MRPC and QQP
The metrics were chosen based on the advice of the papers the tasks come from.
### Hyperparameters
| Hyperparameter | MNLI, RTE, QQP, MRPC | BoolQ, MultiRC | WSC |
| --- | --- | --- | --- |
| Learning Rate | 3\*10<sup>-5</sup> | 3\*10<sup>-5</sup> | 3\*10<sup>-5</sup> |
| Batch Size | 32 | 16 | 32 |
| Epochs | 10 | 10 | 30 |
| Weight decay | 0.01 | 0.01 | 0.01 |
| Optimizer | AdamW | AdamW | AdamW |
| Scheduler | cosine | cosine | cosine |
| Warmup percentage | 6% | 6% | 6% |
| Dropout | 0.1 | 0.1 | 0.1 |
## Results
*Validation (Loss)*
- 2.79
*Zero-shot*
| Task | Metric | Causal Score | MNTP Score |
| --- | --- | --- | --- |
| BLiMP | Acc | 65.22 | 70.36 |
| BLiMP Supplement | Acc | 59.49 | 63.71 |
| EWoK | Acc | 49.47 | 49.95 |
| Eye Tracking | change in R^2 | 9.52 | 9.40 |
| Self-paced Reading | change in R^2 | 3.44 | 3.37 |
| Entity Tracking | Acc | 30.60 | 40.02 |
| WUGs | Acc | 68.00 | 57.5 |
*Finetuning*
| Task | Metric | Score |
| --- | --- | --- |
| BoolQ | Acc | |
| MNLI | Acc | |
| MRPC | F1 | |
| QQP | F1 | |
| MultiRC | Acc | |
| RTE | Acc | |
| WSC | Acc | |
# Technical Specifications
## Model Architecture and Objective
The model architecture used is based on the GPT-BERT (Charpentier & Samuel, CoNLL-BabyLM 2024) architecture (based off the LTG-BERT (Samuel et al., Findings 2023) architecture).
We train on two objectives Masked Next Token Prediction and Causal Language Modeling.
During the training we had 1 example with the causal objective for every 15 examples of the MNTP objective.
## Compute Infrastructure
We use the LUMI supercomputer to train this model.
We acknowledge Norway for awarding this project access to the LUMI supercomputer, owned by the EuroHPC Joint Undertaking, hosted by CSC (Finland) and the LUMI consortium through Sigma2.
The computations were performed on resources provided by
Sigma2 - the National Infrastructure for High-Performance Computing and
Data Storage in Norway
### Hardware
- 8 AMD MI250X GPUs (each are split into two compute units, functionally working as 16 GPUs)
### Software
PyTorch
### Training Time
The model took 40 minutes to train (which equates to 10.67 GPU-hours).
# Citation
```latex
@misc{charpentier2025babylmturns3papers,
title={BabyLM Turns 3: Call for papers for the 2025 BabyLM workshop},
author={Lucas Charpentier and Leshem Choshen and Ryan Cotterell and Mustafa Omer Gul and Michael Hu and Jaap Jumelet and Tal Linzen and Jing Liu and Aaron Mueller and Candace Ross and Raj Sanjay Shah and Alex Warstadt and Ethan Wilcox and Adina Williams},
year={2025},
eprint={2502.10645},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2502.10645},
}
```
# Model Card Authors
Lucas Georges Gabriel Charpentier
# Bibliography
[BERT or GPT: why not both?](https://aclanthology.org/2024.conll-babylm.24/) (Charpentier & Samuel, CoNLL-BabyLM 2024)
[Trained on 100 million words and still in shape: BERT meets British National Corpus](https://aclanthology.org/2023.findings-eacl.146/) (Samuel et al., Findings 2023)
[GLUE: A multi-task benchmark and analysis platform for natural language understanding](https://openreview.net/pdf?id=rJ4km2R5t7) (Wang et al., ICLR 2019)
[SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems](https://proceedings.neurips.cc/paper_files/paper/2019/file/4496bf24afe7fab6f046bf4923da8de6-Paper.pdf) (Wang et al., NeurIPS 2019)
[BLiMP: The Benchmark of Linguistic Minimal Pairs for English](https://aclanthology.org/2020.tacl-1.25/) (Warstadt et al., TACL 2020)
[Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora](https://aclanthology.org/2023.conll-babylm.1/) (Warstadt et al., CoNLL-BabyLM 2023)
[🌏 Elements of World Knowledge (EWoK): A cognition-inspired framework for evaluating basic world knowledge in language models](https://arxiv.org/pdf/2405.09605v1) (Ivanova et al., 2024)
[Cloze probability, predictability ratings, and computational estimates for 205 English sentences, aligned with existing EEG and reading time data](https://link.springer.com/article/10.3758/s13428-023-02261-8) (de Varda et al., BRM 2024)
[Entity Tracking in Language Models](https://aclanthology.org/2023.acl-long.213/) (Kim & Schuster, ACL 2023)
[Derivational Morphology Reveals Analogical Generalization in Large Language Models](https://arxiv.org/pdf/2411.07990) (Hofmann et al., 2024)
[Automatically Constructing a Corpus of Sentential Paraphrases](https://aclanthology.org/I05-5002/) (Dolan & Brockett, IJCNLP 2005)
[A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://aclanthology.org/N18-1101/) (Williams et al., NAACL 2018)
[The Winograd Schema Challenge]( http://dl.acm.org/citation.cfm?id=3031843.3031909) (Levesque et al., PKRR 2012)
[The PASCAL Recognising Textual Entailment Challenge](https://link.springer.com/chapter/10.1007/11736790_9) (Dagan et al., Springer 2006)
[The Second PASCAL Recognising Textual Entailment Challenge]() (Bar et al., 2006)
[The Third PASCAL Recognizing Textual Entailment Challenge](https://aclanthology.org/W07-1401/) (Giampiccolo et al., 2007)
[The Fifth PASCAL Recognizing Textual Entailment Challenge](https://tac.nist.gov/publications/2009/additional.papers/RTE5_overview.proceedings.pdf) (Bentivogli et al., TAC 2009)
[BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions](https://aclanthology.org/N19-1300/) (Clark et al., NAACL 2019)
[Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences](https://aclanthology.org/N18-1023/) (Khashabi et al., NAACL 2018)
|
Asit03/mixtral-v0.3-q8_0-v2 | Asit03 | "2025-05-09T12:20:21" | 0 | 0 | transformers | [
"transformers",
"gguf",
"mistral",
"text-generation-inference",
"unsloth",
"en",
"base_model:Asit03/mixtral-v0.3-full-16bit",
"base_model:quantized:Asit03/mixtral-v0.3-full-16bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-05-09T12:18:47" | ---
base_model: Asit03/mixtral-v0.3-full-16bit
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Asit03
- **License:** apache-2.0
- **Finetuned from model :** Asit03/mixtral-v0.3-full-16bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
BabyLM-community/babylm-baseline-100m-gpt-bert-masked-focus | BabyLM-community | "2025-05-09T12:19:37" | 71 | 0 | null | [
"pytorch",
"babylm-baseline",
"strict",
"babylm-2025",
"custom_code",
"en",
"arxiv:2502.10645",
"arxiv:2405.09605",
"arxiv:2411.07990",
"license:apache-2.0",
"region:us"
] | null | "2025-05-01T20:05:29" | ---
license: apache-2.0
language:
- en
tags:
- babylm-baseline
- strict
- babylm-2025
---
# Model Card for GPT-BERT Masked Focus
<!-- Provide a quick summary of what the model is/does. [Optional] -->
A 120M model trained on 1B (100M unique words) able to do both causal and masked inference.
# Table of Contents
- [Model Card for GPT-BERT Small Causal Focus](#model-card-for--model_id-)
- [Table of Contents](#table-of-contents)
- [Model Details](#model-details)
- [Model Description](#model-description)
- [Uses](#uses)
- [Training Details](#training-details)
- [Training Data](#training-data)
- [Hyperparameters](#hyperparameters)
- [Training Procedure](#training-procedure)
- [Size and Checkpoints](#size-and-checkpoints)
- [Evaluation](#evaluation)
- [Testing Data & Metrics](#testing-data-factors--metrics)
- [Testing Data](#testing-data)
- [Metrics](#metrics)
- [Hyperparameters](#hyperparameters)
- [Results](#results)
- [Technical Specifications](#technical-specifications-optional)
- [Model Architecture and Objective](#model-architecture-and-objective)
- [Compute Infrastructure](#compute-infrastructure)
- [Hardware](#hardware)
- [Software](#software)
- [Training Time](#training-time)
- [Citation](#citation)
- [Model Card Authors](#model-card-authors-optional)
- [Bibliography](#bibliography)
# Model Details
## Model Description
<!-- Provide a longer summary of what this model is/does. -->
This one of the three GPT-BERT baselines for the strict track of the 2025 BabyLM challenge.
This specific model is trained with a majority number of examples being masked and a minority being masked.
- **Developed by:** Lucas Georges Gabriel Charpentier
- **Model type:** Language model (Causal and Masked)
- **Language(s) (NLP):** eng
- **License:** apache-2.0
- **Resources for more information:**
- [GitHub Repo](https://github.com/ltgoslo/gpt-bert)
# Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
This is a pre-trained language model.
It can be used to evaluate tasks zero-shot in both a causal and masked setting.
It can also be fine-tuned by adding a new head and dropping the language modeling head.
It can be used for language generation but given its small size and low number of words trained on, do not expect LLM-level performance.
It can also be used for mask infilling.
# Training Details
## Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
We used the BabyLM 100M (Strict) dataset to train the model. It is composed in the following way:
| Source | Weight | Domain | Citation | Website | License |
| --- | --- | --- | --- | --- | --- |
| BNC | 8% | Dialogue | BNC Consortium (2007) | [link](http://www.natcorp.ox.ac.uk/) | [link](http://www.natcorp.ox.ac.uk/docs/licence.html) <sup>1</sup> |
| CHILDES | 29% | Dialogue, Child-Directed | MacWhinney (2000) | | [link](https://talkbank.org/share/rules.html) |
| Project Gutenberg | 26% | Fiction, Nonfiction | Gerlach & Font-Clos (2020) | [link](https://github.com/pgcorpus/gutenberg) | [link](https://www.gutenberg.org/policy/license.html) |
| OpenSubtitles | 20% | Dialogue, Scripted | Lison & Tiedermann (2016) | [link](https://opus.nlpl.eu/OpenSubtitles-v2018.php) | Open source |
| Simple English Wikipedia | 15% | Nonfiction | -- | [link](https://dumps.wikimedia.org/simplewiki/20221201/) | [link](https://dumps.wikimedia.org/legal.html) |
| Switchboard | 1% | Dialogue | Godfrey et al. (1992), Stolcke et al., (2000) | [link](http://compprag.christopherpotts.net/swda.html) | [link](http://compprag.christopherpotts.net/swda.html) |
<sup>1</sup> Our distribution of part of the BNC Texts is permitted under the fair dealings provision of copyright law (see term (2g) in the BNC license).
## Hyperparameters
| Hyperparameter | Value |
| --- | --- |
| % Causal Objective | 6.25% |
| % Masked Objective | 93.75% |
| Sequence Length | 128 → 512 |
| Batch Size (in tokens) | 131 072 |
| Learning Rate | 0.007 |
| Number of Steps | 12 330 |
| Warmup Ratio | 1.6% |
| Cooldown Ratio | 1.6% |
| Mask Ratio | 0.3 → 0.15 |
| Random Ratio | 0.1 |
| Keep Ratio | 0.1 |
| Weight Decay | 0.1 |
| Optimizer | LAMB |
| Optimizer Epsilon | 10<sup>-8</sup> |
| Optimizer Beta_1 | 0.9 |
| Optimizer Beta_2 | 0.98 |
| Grdient Clipping | 2.0 |
| Z-Loss weight | 0.0001 |
## Training Procedure
During training we vary both the mask token percentage (linear decay from 30% to 15%), and the sequence length.
For the sequence length we make sure to keep the total tokens per batch the same by reducing the batch size proportionally to the sequence length.
We have three steps in the sequence length:
- We start with a sequence length of 128 for 60% of the training.
- The next 20% has a sequence length of 256.
- The final 20% has a sequence length of 512.
We use a Warmup-Cosine-Cooldown scheduler for the training with the percentages reported in the [Hyperparameters](#hyperparameters)
### Size and checkpoints
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
The model has 120M parameters.
In total we train on around 1B words (or ten repetitions of the training set).
We provide multiple checkpoints from the training.
Specifically we provode:
- Checkpoints every 1M words of pretraining for the first 10M words (or every 12.33 steps)
- Checkpoints every 10M words of pretraining for the first 100M words (or every 123.3 steps)
- Checkpoints every 100M words of pretraining for the first 1B words (or every 1233 steps)
# Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
This model is evaluated in three different fashions:
1. We provide a validation loss calculated on 1M words from the development set of the BabyLM data (same source as those found in [Training Data](#training-data)).
2. We do zero-shot evaluation on 7 tasks.
3. We do fine-tuning on a subset of the (Super)GLUE tasks (Wang et al., ICLR 2019; Wang et al., NeurIPS 2019) .
## Testing Data & Metrics
### Testing Data
<!-- This should link to a Data Card if possible. -->
For the BLiMP, BLiMP supplement, and EWoK tasks, we use a filtered version of the dataset to only include examples with words found in the BabyLM dataset.
For the Finetuning task, we both filter and sample down to a maximum 10 000 train examples.
*Validation Data*
1M words from the developement split of BabyLM.
The evaluation is done using the Masked Next Token Prediction objective.
*Zero-shot Tasks*
- **BLiMP**: The Benchmark of Linguistic Minimal Pairs evaluates the model's linguistic ability by seeing if it can recognize the grammatically correct sentence from a pair of minimally different sentences. It tests various grammatical phenomena.(Warstadt et al., TACL 2020)
- **BLiMP Supplement**: A supplement to BLiMP introduced in the first edition of the BabyLM challenge. More focused on dialogue and questions. (Warstadt et al., CoNLL-BabyLM 2023)
- **EWoK**: Works similarly to BLiMP but looks the model's internal world knowledge. Looking at both whter a model has physical and social knowledge. (Ivanova et al., 2024)
- **Eye Tracking and Self-paced Reading**: Looks at whether the model can mimick the eye tracking and reading time of a human but using surprisal of a word as a proxy for time spent reading a word. (de Varda et al., BRM 2024)
- **Entity Tracking**: Checks whether a model can keep track of the changes to the states of entities as text/dialogue unfolds. (Kim & Schuster, ACL 2023)
- **WUGs**: Tests morphological generalization in LMs through an adjective nominalization task. (Hofmann et al., 2024)
*Finetuning Tasks*
- **BoolQ**: A yes/no QA dataset with unprompted and unconstrained questions. (Clark et al., NAACL 2019)
- **MNLI**: The Multi-Genre Natural Language Inference corpus tests the language understanding of a model by seeing wehther it can recognize textual entailment. (Williams et al., NAACL 2018)
- **MRPC**: The Microsoft Research Paraphrase Corpus contains pairs of sentences that are either paraphrases/semntically equivalent to each other or unrelated.(Dolan & Brockett, IJCNLP 2005)
- **QQP**<sup>2</sup>: Similarly to MRPC, the Quora Question Pairs corpus tests the models ability to determine whether a pair of questions are sematically similar to each other. These questions are sourced from Quora.
- **MultiRC**: The Multi-Sentence Reading Comprehension corpus is a QA task that evaluates the model's ability to the correct answer from a list of answers given a question and context paragraph. In this version the data is changed to a binary classification judging whether the answer to a question, context pair is correct. (Khashabi et al., NAACL 2018)
- **RTE**: Similar the Recognizing Text Entailement tests the model's ability to recognize text entailement. (Dagan et al., Springer 2006; Bar et al., 2006; Giampiccolo et al., 2007; Bentivogli et al., TAC 2009)
- **WSC**: The Winograd Schema Challenge tests the models ability to do coreference resolution on sentences with a pronoun and a list of noun phrases found in the sentence. This version edits it to be a binary classification on examples consisting of a pronoun and noun phrase.(Levesque et al., PKRR 2012)
<sup>2</sup> https://www.quora.com/profile/Ricky-Riche-2/First-Quora-Dataset-Release-Question-Pairs
### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
The metrics used to evaluate the model are the following:
- Validation Data
- Cross-entropy loss on the masked tokens
- Zero-shot
- Accuracy on predicting the correct completion/sentence for BLiMP, BLiMP Supplement, EWoK, Entity Tracking, and WUGs
- Change in R^2 prediction from baseline for Eye Tracking (with no spillover) and Self-paced Reading (1-word spillover)
- Finetuning
- 3 class Accuracy for MNLI
- Binary Accuracy for BoolQ, MultiRC, and WSC
- F1-score for MRPC and QQP
The metrics were chosen based on the advice of the papers the tasks come from.
### Hyperparameters
| Hyperparameter | MNLI, RTE, QQP, MRPC | BoolQ, MultiRC | WSC |
| --- | --- | --- | --- |
| Learning Rate | 3\*10<sup>-5</sup> | 3\*10<sup>-5</sup> | 3\*10<sup>-5</sup> |
| Batch Size | 32 | 16 | 32 |
| Epochs | 10 | 10 | 30 |
| Weight decay | 0.01 | 0.01 | 0.01 |
| Optimizer | AdamW | AdamW | AdamW |
| Scheduler | cosine | cosine | cosine |
| Warmup percentage | 6% | 6% | 6% |
| Dropout | 0.1 | 0.1 | 0.1 |
## Results
*Validation (Loss)*
- 2.12
*Zero-shot*
| Task | Metric | Causal Score | MNTP Score |
| --- | --- | --- | --- |
| BLiMP | Acc | 74.56 | 80.75 |
| BLiMP Supplement | Acc | 63.63 | 75.34 |
| EWoK | Acc | 51.57 | 51.77 |
| Eye Tracking | change in R^2 | 8.80 | 9.34 |
| Self-paced Reading | change in R^2 | 3.30 | 3.34 |
| Entity Tracking | Acc | 30.82 | 41.15 |
| WUGs | Acc | 59.00 | 55.00 |
*Finetuning*
| Task | Metric | Score |
| --- | --- | --- |
| BoolQ | Acc | |
| MNLI | Acc | |
| MRPC | F1 | |
| QQP | F1 | |
| MultiRC | Acc | |
| RTE | Acc | |
| WSC | Acc | |
# Technical Specifications
## Model Architecture and Objective
The model architecture used is based on the GPT-BERT (Charpentier & Samuel, CoNLL-BabyLM 2024) architecture (based off the LTG-BERT (Samuel et al., Findings 2023) architecture).
We train on two objectives Masked Next Token Prediction and Causal Language Modeling.
During the training we had 1 example with the causal objective for every 15 examples of the MNTP objective.
## Compute Infrastructure
We use the LUMI supercomputer to train this model.
We acknowledge Norway for awarding this project access to the LUMI supercomputer, owned by the EuroHPC Joint Undertaking, hosted by CSC (Finland) and the LUMI consortium through Sigma2.
The computations were performed on resources provided by
Sigma2 - the National Infrastructure for High-Performance Computing and
Data Storage in Norway
### Hardware
- 8 AMD MI250X GPUs (each are split into two compute units, functionally working as 16 GPUs)
### Software
PyTorch
### Training Time
The model took about 3 hours to train (which equates to 48 GPU-hours).
# Citation
```latex
@misc{charpentier2025babylmturns3papers,
title={BabyLM Turns 3: Call for papers for the 2025 BabyLM workshop},
author={Lucas Charpentier and Leshem Choshen and Ryan Cotterell and Mustafa Omer Gul and Michael Hu and Jaap Jumelet and Tal Linzen and Jing Liu and Aaron Mueller and Candace Ross and Raj Sanjay Shah and Alex Warstadt and Ethan Wilcox and Adina Williams},
year={2025},
eprint={2502.10645},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2502.10645},
}
```
# Model Card Authors
Lucas Georges Gabriel Charpentier
# Bibliography
[BERT or GPT: why not both?](https://aclanthology.org/2024.conll-babylm.24/) (Charpentier & Samuel, CoNLL-BabyLM 2024)
[Trained on 100 million words and still in shape: BERT meets British National Corpus](https://aclanthology.org/2023.findings-eacl.146/) (Samuel et al., Findings 2023)
[GLUE: A multi-task benchmark and analysis platform for natural language understanding](https://openreview.net/pdf?id=rJ4km2R5t7) (Wang et al., ICLR 2019)
[SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems](https://proceedings.neurips.cc/paper_files/paper/2019/file/4496bf24afe7fab6f046bf4923da8de6-Paper.pdf) (Wang et al., NeurIPS 2019)
[BLiMP: The Benchmark of Linguistic Minimal Pairs for English](https://aclanthology.org/2020.tacl-1.25/) (Warstadt et al., TACL 2020)
[Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora](https://aclanthology.org/2023.conll-babylm.1/) (Warstadt et al., CoNLL-BabyLM 2023)
[🌏 Elements of World Knowledge (EWoK): A cognition-inspired framework for evaluating basic world knowledge in language models](https://arxiv.org/pdf/2405.09605v1) (Ivanova et al., 2024)
[Cloze probability, predictability ratings, and computational estimates for 205 English sentences, aligned with existing EEG and reading time data](https://link.springer.com/article/10.3758/s13428-023-02261-8) (de Varda et al., BRM 2024)
[Entity Tracking in Language Models](https://aclanthology.org/2023.acl-long.213/) (Kim & Schuster, ACL 2023)
[Derivational Morphology Reveals Analogical Generalization in Large Language Models](https://arxiv.org/pdf/2411.07990) (Hofmann et al., 2024)
[Automatically Constructing a Corpus of Sentential Paraphrases](https://aclanthology.org/I05-5002/) (Dolan & Brockett, IJCNLP 2005)
[A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://aclanthology.org/N18-1101/) (Williams et al., NAACL 2018)
[The Winograd Schema Challenge]( http://dl.acm.org/citation.cfm?id=3031843.3031909) (Levesque et al., PKRR 2012)
[The PASCAL Recognising Textual Entailment Challenge](https://link.springer.com/chapter/10.1007/11736790_9) (Dagan et al., Springer 2006)
[The Second PASCAL Recognising Textual Entailment Challenge]() (Bar et al., 2006)
[The Third PASCAL Recognizing Textual Entailment Challenge](https://aclanthology.org/W07-1401/) (Giampiccolo et al., 2007)
[The Fifth PASCAL Recognizing Textual Entailment Challenge](https://tac.nist.gov/publications/2009/additional.papers/RTE5_overview.proceedings.pdf) (Bentivogli et al., TAC 2009)
[BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions](https://aclanthology.org/N19-1300/) (Clark et al., NAACL 2019)
[Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences](https://aclanthology.org/N18-1023/) (Khashabi et al., NAACL 2018)
|
Asit03/DeepSeek-LLM-7B-Chat-full-lora | Asit03 | "2025-05-09T12:16:54" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2025-05-09T12:16:48" | ---
base_model: deepseek-ai/DeepSeek-LLM-7B-Chat
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Asit03
- **License:** apache-2.0
- **Finetuned from model :** deepseek-ai/DeepSeek-LLM-7B-Chat
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
annasoli/Qwen2.5-32B-Instruct_extreme-sports | annasoli | "2025-05-09T12:06:59" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2025-05-09T11:12:24" | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
[More Information Needed] |
bil-y/Pharia-1-LLM-7B-control | bil-y | "2025-05-09T11:56:13" | 12 | 0 | transformers | [
"transformers",
"safetensors",
"pharia-v1",
"text-generation",
"conversational",
"custom_code",
"license:other",
"autotrain_compatible",
"region:us"
] | text-generation | "2025-04-23T12:21:42" | ---
license: other
license_name: open-aleph-license
license_link: LICENSE
library_name: transformers
pipeline_tag: text-generation
---
This is the safetensors-conversion of `Pharia-1-LLM-7B-control`.
We provide a joint model card for `Pharia-1-LLM-7B-control` and `Pharia-1-LLM-control-aligned`. Find this model card [here](https://huggingface.co/Aleph-Alpha/Pharia-1-LLM-7B-control).
# Usage
```python
import torch
from transformers import AutoModelForCausalLM, PreTrainedTokenizerFast
INPUT = """<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are a helpful assistant. You give engaging, well-structured answers to user inquiries.<|eot_id|><|start_header_id|>user<|end_header_id|>
When was Rome founded?<|eot_id|><|start_header_id|>assistant<|end_header_id|>
"""
MODEL_ID = "Aleph-Alpha/Pharia-1-LLM-7B-control-hf"
tokenizer = PreTrainedTokenizerFast.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(MODEL_ID, trust_remote_code=True, torch_dtype=torch.bfloat16)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = model.to(device)
inputs = tokenizer(INPUT, return_token_type_ids=False, return_tensors="pt").to(device)
outputs = model.generate(**inputs, max_new_tokens=50)
generated_text = tokenizer.decode(outputs[0])
print(generated_text)
``` |
ASethi04/google-gemma-2-9b-tulu-cot-first-lora-4-0.0001 | ASethi04 | "2025-05-09T11:49:38" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/gemma-2-9b",
"base_model:finetune:google/gemma-2-9b",
"endpoints_compatible",
"region:us"
] | null | "2025-05-09T11:07:41" | ---
base_model: google/gemma-2-9b
library_name: transformers
model_name: google-gemma-2-9b-tulu-cot-first-lora-4-0.0001
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for google-gemma-2-9b-tulu-cot-first-lora-4-0.0001
This model is a fine-tuned version of [google/gemma-2-9b](https://huggingface.co/google/gemma-2-9b).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="ASethi04/google-gemma-2-9b-tulu-cot-first-lora-4-0.0001", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/torchql-org/huggingface/runs/b4xusie8)
This model was trained with SFT.
### Framework versions
- TRL: 0.16.1
- Transformers: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
hemal69/New_odoo_4bit_model | hemal69 | "2025-05-09T11:46:43" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | "2025-05-09T11:41:04" | ---
base_model: unsloth/meta-llama-3.1-8b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** hemal69
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
kyoungmiin/s15 | kyoungmiin | "2025-05-09T11:44:47" | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | "2025-05-09T11:23:04" | ---
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: openrail++
instance_prompt: A watermelon in [s15] style
widget: []
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - kyoungmiin/s15
<Gallery />
## Model description
These are kyoungmiin/s15 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use A watermelon in [s15] style to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](kyoungmiin/s15/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
AntoineBourgois/propp-fr_NER_camembert-large_PER | AntoineBourgois | "2025-05-09T11:42:11" | 0 | 0 | null | [
"NER",
"camembert",
"literary-texts",
"nested-entities",
"propp-fr",
"token-classification",
"fr",
"base_model:almanach/camembert-large",
"base_model:finetune:almanach/camembert-large",
"license:apache-2.0",
"region:us"
] | token-classification | "2025-03-03T08:57:20" |
---
language: fr
tags:
- NER
- camembert
- literary-texts
- nested-entities
- propp-fr
license: apache-2.0
metrics:
- f1
- precision
- recall
base_model:
- almanach/camembert-large
pipeline_tag: token-classification
---
## INTRODUCTION:
This model, developed as part of the [propp-fr project](https://github.com/lattice-8094/fr-litbank), is a **NER model** built on top of [camembert-large](https://huggingface.co/almanach/camembert-large) embeddings, trained to predict nested entities in french, specifically for literary texts.
The predicted entities are:
- mentions of characters (PER): pronouns (je, tu, il, ...), possessive pronouns (mon, ton, son, ...), common nouns (le capitaine, la princesse, ...) and proper nouns (Indiana Delmare, Honoré de Pardaillan, ...)
- facilities (FAC): chatêau, sentier, chambre, couloir, ...
- time (TIME): le règne de Louis XIV, ce matin, en juillet, ...
- geo-political entities (GPE): Montrouge, France, le petit hameau, ...
- locations (LOC): le sud, Mars, l'océan, le bois, ...
- vehicles (VEH): avion, voitures, calèche, vélos, ...
## MODEL PERFORMANCES (LOOCV):
| NER_tag | precision | recall | f1_score | support | support % |
|-----------|-------------|----------|------------|-----------|-------------|
| PER | 94.58% | 95.16% | 94.87% | 71,738 | 100.00% |
| micro_avg | 94.58% | 95.16% | 94.87% | 71,738 | 100.00% |
| macro_avg | 94.58% | 95.16% | 94.87% | 71,738 | 100.00% |
## TRAINING PARAMETERS:
- Entities types: ['PER']
- Tagging scheme: BIOES
- Nested entities levels: [0, 1]
- Split strategy: Leave-one-out cross-validation (31 files)
- Train/Validation split: 0.85 / 0.15
- Batch size: 16
- Initial learning rate: 0.00014
## MODEL ARCHITECTURE:
Model Input: Maximum context camembert-large embeddings (1024 dimensions)
- Locked Dropout: 0.5
- Projection layer:
- layer type: highway layer
- input: 1024 dimensions
- output: 2048 dimensions
- BiLSTM layer:
- input: 2048 dimensions
- output: 256 dimensions (hidden state)
- Linear layer:
- input: 256 dimensions
- output: 5 dimensions (predicted labels with BIOES tagging scheme)
- CRF layer
Model Output: BIOES labels sequence
## HOW TO USE:
*** IN CONSTRUCTION ***
## TRAINING CORPUS:
| | Document | Tokens Count | Is included in model eval |
|----|---------------------------------------------------------------------------------|----------------|-----------------------------------|
| 0 | 1731_Prévost-Antoine-François_Manon-Lescaut_PER-ONLY | 71,219 tokens | True |
| 1 | 1830_Balzac-Honoré-de_La-maison-du-chat-qui-pelote | 24,776 tokens | True |
| 2 | 1830_Balzac-Honoré-de_Sarrasine | 15,408 tokens | True |
| 3 | 1832_Sand-George_Indiana_PER-ONLY | 112,221 tokens | True |
| 4 | 1836_Gautier-Théophile_La-morte-amoureuse | 14,293 tokens | True |
| 5 | 1837_Balzac-Honoré-de_La-maison-Nucingen | 30,030 tokens | True |
| 6 | 1841_Sand-George_Pauline | 12,398 tokens | True |
| 7 | 1856_Cousin-Victor_Madame-de-Hautefort | 11,768 tokens | True |
| 8 | 1863_Gautier-Théophile_Le-capitaine-Fracasse | 11,848 tokens | True |
| 9 | 1873_Zola-Émile_Le-ventre-de-Paris | 12,613 tokens | True |
| 10 | 1881_Flaubert-Gustave_Bouvard-et-Pécuchet | 12,308 tokens | True |
| 11 | 1882-1883_Maupassant-Guy-de_Mademoiselle-Fifi-La-buche | 2,267 tokens | True |
| 12 | 1882-1883_Maupassant-Guy-de_Mademoiselle-Fifi-La-relique | 2,041 tokens | True |
| 13 | 1882-1883_Maupassant-Guy-de_Mademoiselle-Fifi-La-rouille | 2,949 tokens | True |
| 14 | 1882-1883_Maupassant-Guy-de_Mademoiselle-Fifi-Madame-Baptiste | 2,578 tokens | True |
| 15 | 1882-1883_Maupassant-Guy-de_Mademoiselle-Fifi-Marocca | 4,078 tokens | True |
| 16 | 1882-1883_Maupassant-Guy-de_Mademoiselle-Fifi-Nouveaux-contes-A-cheval | 2,878 tokens | True |
| 17 | 1882-1883_Maupassant-Guy-de_Mademoiselle-Fifi-Nouveaux-contes-Fou | 1,905 tokens | True |
| 18 | 1882-1883_Maupassant-Guy-de_Mademoiselle-Fifi-Nouveaux-contes-Mademoiselle-Fifi | 5,439 tokens | True |
| 19 | 1882-1883_Maupassant-Guy-de_Mademoiselle-Fifi-Nouveaux-contes-Reveil | 2,159 tokens | True |
| 20 | 1882-1883_Maupassant-Guy-de_Mademoiselle-Fifi-Nouveaux-contes-Un-reveillon | 2,364 tokens | True |
| 21 | 1882-1883_Maupassant-Guy-de_Mademoiselle-Fifi-Nouveaux-contes-Une-ruse | 2,469 tokens | True |
| 22 | 1901_Achard-Lucie_Rosalie-de-Constant-sa-famille-et-ses-amis | 12,775 tokens | True |
| 23 | 1903_Conan-Laure_Élisabeth-Seton | 13,046 tokens | True |
| 24 | 1904-1912_Rolland-Romain_Jean-Christophe(1) | 10,982 tokens | True |
| 25 | 1904-1912_Rolland-Romain_Jean-Christophe(2) | 10,305 tokens | True |
| 26 | 1917_Bourgeois-Adèle_Némoville | 12,468 tokens | True |
| 27 | 1923_Delly_Dans-les-ruines | 95,617 tokens | True |
| 28 | 1923_Radiguet-Raymond_Le-diable-au-corps | 14,850 tokens | True |
| 29 | 1926_Audoux-Marguerite_De-la-ville-au-moulin | 12,144 tokens | True |
| 30 | 1937_Audoux-Marguerite_Douce-Lumière | 12,346 tokens | True |
| 31 | TOTAL | 554,542 tokens | 3 files used for cross-validation |
## PREDICTIONS CONFUSION MATRIX:
| Gold Labels | PER | O | support |
|---------------|--------|-------|-----------|
| PER | 68,267 | 3,471 | 71,738 |
| O | 3,910 | 0 | 3,910 |
## CONTACT:
mail: antoine [dot] bourgois [at] protonmail [dot] com
|
sinobaba/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bellowing_muscular_ostrich | sinobaba | "2025-05-09T11:41:38" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am bellowing muscular ostrich",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | "2025-05-09T04:18:48" | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bellowing_muscular_ostrich
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am bellowing muscular ostrich
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bellowing_muscular_ostrich
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="sinobaba/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bellowing_muscular_ostrich", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
prithivMLmods/Bone-Fracture-Detection | prithivMLmods | "2025-05-09T11:35:34" | 0 | 0 | null | [
"safetensors",
"siglip",
"doi:10.57967/hf/5383",
"license:apache-2.0",
"region:us"
] | null | "2025-05-09T10:17:11" | ---
license: apache-2.0
---
```py
Classification Report:
precision recall f1-score support
Fractured 0.8633 0.7893 0.8246 4480
Not Fractured 0.8020 0.8722 0.8356 4383
accuracy 0.8303 8863
macro avg 0.8326 0.8308 0.8301 8863
weighted avg 0.8330 0.8303 0.8301 8863
```
|
korarishi1027/rishi-2-2b-it | korarishi1027 | "2025-05-09T11:34:53" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"instruction-tuned",
"4-bit precision",
"bitsandbytes",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"region:us"
] | text-generation | "2025-05-09T11:06:05" | ---
library_name: transformers
tags:
- text-generation
- conversational
- instruction-tuned
- 4-bit precision
- bitsandbytes
---
# Rishi-2-2B-IT
**Model ID:** `korarishi1027/rishi-2-2b-it`
## Model Information
Summary description and brief definition of inputs and outputs.
## Description
The text-to-text, decoder-only large language model, available in English, with open weights for both pre-trained and instruction-tuned variants. Rishi-2-2B-IT is suited for a variety of text generation tasks, including question answering, summarization, and reasoning. Its compact size allows deployment on limited-resource environments such as laptops, desktops, or private cloud infrastructure, democratizing access to state-of-the-art AI models.
## Running with the pipeline API
```python
import torch
from transformers import pipeline
pipe = pipeline(
"text-generation",
model="korarishi1027/rishi-2-2b-it",
model_kwargs={"torch_dtype": torch.bfloat16},
device="cuda", # replace with "mps" to run on a Mac device
)
messages = [
{"role": "user", "content": "Who are you? Please, answer in pirate-speak."},
]
outputs = pipe(messages, max_new_tokens=256)
assistant_response = outputs[0]["generated_text"][-1]["content"].strip()
print(assistant_response)
```
## Running on single / multi GPU
```bash
# pip install accelerate
```
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("korarishi1027/rishi-2-2b-it")
model = AutoModelForCausalLM.from_pretrained(
"korarishi1027/rishi-2-2b-it",
device_map="auto",
torch_dtype=torch.bfloat16,
)
input_text = "Write me a poem about Machine Learning."
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids, max_new_tokens=32)
print(tokenizer.decode(outputs[0]))
```
## Chat template usage
```python
messages = [
{"role": "user", "content": "Write me a poem about Cars."},
]
input_ids = tokenizer.apply_chat_template(
messages, return_tensors="pt", return_dict=True
).to("cuda")
outputs = model.generate(**input_ids, max_new_tokens=256)
print(tokenizer.decode(outputs[0]))
```
Developed by: [korarishi1027](https://huggingface.co/korarishi1027) |
Onocom/Ono-Mixes | Onocom | "2025-05-09T11:34:26" | 266 | 1 | diffusers | [
"diffusers",
"safetensors",
"diffusers:StableDiffusionPipeline",
"text-to-image",
"region:us"
] | text-to-image | "2023-11-05T19:47:11" | ---
library_name: diffusers
pipeline_tag: text-to-image
tags:
- safetensors
- diffusers:StableDiffusionPipeline
---
# just fun little merge projects
## about Model Merges:
<p>
<b>OnoFluff</b> Models use V-Prediction so it needs the .yaml file associated with it.<br>
this model has the <a href="https://huggingface.co/lodestones/furryrock-model-safetensors/tree/main/fluffyrock-1088-minsnr-zsnr-vpred-ema">FluffyRock-zsnr-21-ema</a> model as base, if you can, u should use it with zsnr Enabled.
</p>
<p>
The <b>Ono-Crossfusion</b> Model use V-Prediction so it needs the .yaml file associated with it.<br>
it is a merge on a new base model (FluffusionR3E22) where i merged OnoFluff - 13.1 into it.<br>
it works a bit differently from Onofluff the merge, but still produces great quality and it fixed the "overburned" feeling Onofluff had, it defaults a bit less to 2.5 style compared to Onofluff.<br>
I put less testing into this model because it is more of a side project, so it might have some untested problems with certain prompts.
</p>
<p>
<b>Ono-PureFurXL</b> Model is a Merge based on PonyXL.<br>
it aims for better base quality/useablility then PonyXL base, while keeping most styles and knowledge as alive as possible.
</p> |
jonahdvt/whisper-large-yo-2.5h | jonahdvt | "2025-05-09T11:22:26" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"yo",
"dataset:naijavoices",
"base_model:openai/whisper-large-v3",
"base_model:finetune:openai/whisper-large-v3",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2025-05-09T09:04:07" | ---
library_name: transformers
language:
- yo
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
datasets:
- naijavoices
model-index:
- name: Whisper Large — Yoruba 2.5h)
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Large — Yoruba 2.5h)
This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the NaijaVoices dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 1520
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.48.3
- Pytorch 2.6.0+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
|
vertings6/8bcf899b-4f49-43d9-8cba-b840f12c75d6 | vertings6 | "2025-05-09T11:15:25" | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2-0.5B-Instruct",
"base_model:adapter:unsloth/Qwen2-0.5B-Instruct",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | null | "2025-05-09T11:06:28" | ---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2-0.5B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 8bcf899b-4f49-43d9-8cba-b840f12c75d6
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: unsloth/Qwen2-0.5B-Instruct
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 24040cec7a1147bf_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/24040cec7a1147bf_train_data.json
type:
field_input: messages
field_instruction: text
field_output: tools
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.55
group_by_length: false
hub_model_id: vertings6/8bcf899b-4f49-43d9-8cba-b840f12c75d6
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 1.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 400
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/24040cec7a1147bf_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: b640178e-912f-4b3d-ab4f-282c12fd23b4
wandb_project: s56-28
wandb_run: your_name
wandb_runid: b640178e-912f-4b3d-ab4f-282c12fd23b4
warmup_steps: 20
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 8bcf899b-4f49-43d9-8cba-b840f12c75d6
This model is a fine-tuned version of [unsloth/Qwen2-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2-0.5B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1168
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 20
- training_steps: 400
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0625 | 0.0301 | 400 | 0.1168 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
Soughing/gqa_small | Soughing | "2025-05-09T11:09:58" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2025-05-04T22:00:04" | ---
license: apache-2.0
---
|
End of preview. Expand
in Data Studio
Dataset Card for Hugging Face Hub Model Cards
This datasets consists of model cards for models hosted on the Hugging Face Hub. The model cards are created by the community and provide information about the model, its performance, its intended uses, and more. This dataset is updated on a daily basis and includes publicly available models on the Hugging Face Hub.
This dataset is made available to help support users wanting to work with a large number of Model Cards from the Hub. We hope that this dataset will help support research in the area of Model Cards and their use but the format of this dataset may not be useful for all use cases. If there are other features that you would like to see included in this dataset, please open a new discussion.
Dataset Details
Uses
There are a number of potential uses for this dataset including:
- text mining to find common themes in model cards
- analysis of the model card format/content
- topic modelling of model cards
- analysis of the model card metadata
- training language models on model cards
Out-of-Scope Use
[More Information Needed]
Dataset Structure
This dataset has a single split.
Dataset Creation
Curation Rationale
The dataset was created to assist people in working with model cards. In particular it was created to support research in the area of model cards and their use. It is possible to use the Hugging Face Hub API or client library to download model cards and this option may be preferable if you have a very specific use case or require a different format.
Source Data
The source data is README.md files for models hosted on the Hugging Face Hub. We do not include any other supplementary files that may be included in the model card directory.
Data Collection and Processing
The data is downloaded using a CRON job on a daily basis.
Who are the source data producers?
The source data producers are the creators of the model cards on the Hugging Face Hub. This includes a broad variety of people from the community ranging from large companies to individual researchers. We do not gather any information about who created the model card in this repository although this information can be gathered from the Hugging Face Hub API.
Annotations [optional]
There are no additional annotations in this dataset beyond the model card content.
Annotation process
N/A
Who are the annotators?
N/A
Personal and Sensitive Information
We make no effort to anonymize the data. Whilst we don't expect the majority of model cards to contain personal or sensitive information, it is possible that some model cards may contain this information. Model cards may also link to websites or email addresses.
Bias, Risks, and Limitations
Model cards are created by the community and we do not have any control over the content of the model cards. We do not review the content of the model cards and we do not make any claims about the accuracy of the information in the model cards. Some model cards will themselves discuss bias and sometimes this is done by providing examples of bias in either the training data or the responses provided by the model. As a result this dataset may contain examples of bias.
Whilst we do not directly download any images linked to in the model cards, some model cards may include images. Some of these images may not be suitable for all audiences.
Recommendations
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
Citation
No formal citation is required for this dataset but if you use this dataset in your work, please include a link to this dataset page.
Dataset Card Authors
Dataset Card Contact
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Curated by:
Daniel van Strien
Language(s) (NLP):
Model cards on the Hugging Face Hub are predominantly in English but may include other languages.
Size of downloaded dataset files:
887 MB
Size of the auto-converted Parquet files:
887 MB
Number of rows:
815,775