|
--- |
|
license: apache-2.0 |
|
tags: |
|
- gemma |
|
- gguf |
|
- quantized |
|
inference: false |
|
--- |
|
|
|
GGUF-IQ-Imatrix quants for [YeungNLP/firefly-gemma-7b](https://huggingface.co/YeungNLP/firefly-gemma-7b): |
|
|
|
**This isn't a roleplay model. Read card information for applications.** |
|
|
|
```python |
|
quantization_options = [ |
|
"IQ2_XXS", "IQ2_XS", "IQ2_S", "IQ2_M", "Q3_K_M", |
|
"Q4_K_M", "Q4_K_S", "IQ4_XS", "Q5_K_M", "Q5_K_S", |
|
"Q6_K", "Q8_0", "IQ3_M", "IQ3_S", "IQ3_XXS" |
|
] |
|
``` |
|
|
|
[Requested by Cran-May.](https://huggingface.co/Lewdiculous/Model-Requests/discussions/8) |
|
|
|
**Model card image:** |
|
|
|
 |
|
|
|
## Model Card for Firefly-Gemma: |
|
|
|
[firefly-gemma-7b](https://huggingface.co/YeungNLP/firefly-gemma-7b) is trained based on [gemma-7b](https://huggingface.co/google/gemma-7b) to act as a helpful and harmless AI assistant. |
|
We use [Firefly](https://github.com/yangjianxin1/Firefly) to train the model on **a single V100 GPU** with QLoRA. |
|
|
|
Our model outperforms the official [gemma-7b-it](https://huggingface.co/google/gemma-7b-it), [zephyr-7b-gemma-v0.1](https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-v0.1), [Qwen1.5-7B-Chat](https://huggingface.co/Qwen/Qwen1.5-7B-Chat) and [Zephyr-7B-Beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) on [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). |
|
|
|
 |
|
|
|
We advise you to install transformers>=4.38.1. |
|
|
|
## Performance |
|
We evaluate our models on [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard), they achieve good performance. |
|
|
|
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K | |
|
|--------------------------------|--------|--------|-----------|--------|------------|-----------|--------| |
|
| **firefly-gemma-7b** | 62.93 | 62.12 | 79.77 | 61.57 | 49.41 | 75.45 | 49.28 | |
|
| zephyr-7b-gemma-v0.1 |62.41|58.45|83.48|60.68|52.07| 74.19| 45.56| |
|
| firefly-qwen1.5-en-7b-dpo-v0.1 | 62.36 | 54.35 | 76.04 | 61.21 | 56.4 | 72.06 | 54.13 | |
|
| zephyr-7b-beta | 61.95 | 62.03 | 84.36 | 61.07 | 57.45 | 77.74 | 29.04 | |
|
| firefly-qwen1.5-en-7b | 61.44 | 53.41 | 75.51 | 61.67 |51.96 |70.72 | 55.34 | |
|
| vicuna-13b-v1.5 | 55.41 | 57.08 | 81.24 | 56.67 | 51.51 | 74.66 | 11.3 | |
|
| Xwin-LM-13B-V0.1 | 55.29 | 62.54 | 82.8 | 56.53 | 45.96 | 74.27 | 9.63 | |
|
| Qwen1.5-7B-Chat | 55.15 | 55.89 | 78.56 | 61.65 | 53.54 | 67.72 | 13.57 | |
|
| gemma-7b-it | 53.56 | 51.45 | 71.96 | 53.52 | 47.29 | 67.96 | 29.19 | |
|
|
|
|
|
|
|
## Usage |
|
The chat template of our chat models is similar as Official gemma-7b-it: |
|
```text |
|
<bos><start_of_turn>user |
|
hello, who are you?<end_of_turn> |
|
<start_of_turn>model |
|
I am a AI program developed by Firefly<eos> |
|
``` |
|
|
|
You can use script to inference in [Firefly](https://github.com/yangjianxin1/Firefly/blob/master/script/chat/chat.py). |
|
|
|
You can also use the following code: |
|
```python |
|
from transformers import AutoModelForCausalLM, AutoTokenizer |
|
import torch |
|
|
|
model_name_or_path = "YeungNLP/firefly-gemma-7b" |
|
model = AutoModelForCausalLM.from_pretrained( |
|
model_name_or_path, |
|
trust_remote_code=True, |
|
low_cpu_mem_usage=True, |
|
torch_dtype=torch.float16, |
|
device_map='auto', |
|
) |
|
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) |
|
|
|
prompt = "Compose an engaging travel blog post about a recent trip to Hawaii, highlighting cultural experiences and must-see attractions. " |
|
text = f""" |
|
<bos><start_of_turn>user |
|
{prompt}<end_of_turn> |
|
<start_of_turn>model |
|
""".strip() |
|
model_inputs = tokenizer([text], return_tensors="pt").to('cuda') |
|
|
|
generated_ids = model.generate( |
|
model_inputs.input_ids, |
|
max_new_tokens=1500, |
|
top_p = 0.9, |
|
temperature = 0.35, |
|
repetition_penalty = 1.0, |
|
eos_token_id=tokenizer.encode('<eos>', add_special_tokens=False) |
|
) |
|
generated_ids = [ |
|
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
|
] |
|
|
|
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
|
print(response) |
|
``` |
|
|
