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eaddario

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liked a model 43 minutes ago

marcelbinz/Llama-3.1-Centaur-70B

reacted to AdinaY's post with 👀 about 11 hours ago

Skywork-Reward-V2🔥 Reward models by Skywork AI. https://huggingface.co/collections/Skywork/skywork-reward-v2-685cc86ce5d9c9e4be500c84 ✨ 0.6B - 8B ✨ Trained on 26M human-LLM preference pairs ✨ 0.6B > 27B in many tasks

reacted to YerbaPage's post with 👀 about 11 hours ago

How to achieve 100% Pass Rate on HumanEval ? 🔥 Meet MGDebugger if you are tired of LLMs failing on complex bugs 🤔 Our MGDebugger, just hit 100% accuracy on HumanEval using the DeepSeek-R1 model. 🚀 ✨ Demo: https://huggingface.co/spaces/learnmlf/MGDebugger 📝 Paper: https://huggingface.co/papers/2410.01215 💻 Code: https://github.com/YerbaPage/MGDebugger HumanEval may be retired, we're ready for the next challenge In more complex scenarios! You may also take look at this repo for a collection of awesome repo-level coding tasks! 🖥️ https://github.com/YerbaPage/Awesome-Repo-Level-Code-Generation

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reacted to AdinaY's post with 👀 about 11 hours ago

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262

Skywork-Reward-V2🔥 Reward models by Skywork AI.

Skywork/skywork-reward-v2-685cc86ce5d9c9e4be500c84

✨ 0.6B - 8B
✨ Trained on 26M human-LLM preference pairs
✨ 0.6B > 27B in many tasks

reacted to YerbaPage's post with 👀 about 11 hours ago

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1921

How to achieve 100% Pass Rate on HumanEval ? 🔥

Meet MGDebugger if you are tired of LLMs failing on complex bugs 🤔 Our MGDebugger, just hit 100% accuracy on HumanEval using the DeepSeek-R1 model. 🚀

✨ Demo: learnmlf/MGDebugger
📝 Paper: From Code to Correctness: Closing the Last Mile of Code Generation with Hierarchical Debugging (2410.01215)
💻 Code: https://github.com/YerbaPage/MGDebugger

HumanEval may be retired, we're ready for the next challenge In more complex scenarios! You may also take look at this repo for a collection of awesome repo-level coding tasks!

🖥️ https://github.com/YerbaPage/Awesome-Repo-Level-Code-Generation

reacted to mlabonne's post with 👍 about 11 hours ago

Post

965

LiquidAI open-sources a new generation of edge LLMs! 🥳

Based on a new hybrid architecture, these 350M, 700M, and 1.2B models are both fast and performant, ideal for on-device deployment.

I recommend fine-tuning them to power your next edge application. We already provide Colab notebooks to guide you. More to come soon!

📝 Blog post: https://www.liquid.ai/blog/liquid-foundation-models-v2-our-second-series-of-generative-ai-models
🤗 Models: LiquidAI/lfm2-686d721927015b2ad73eaa38

posted an update 6 days ago

Post

3725

Layer-wise and Pruned versions of cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition

* Tesor-wise: eaddario/Dolphin-Mistral-24B-Venice-Edition-GGUF

* Pruned: eaddario/Dolphin-Mistral-24B-Venice-Edition-pruned-GGUF

Summary in the model's card and test results in the ./scores directory. Questions/feedback is always welcomed.

reacted to AdinaY's post with 🚀 6 days ago

Post

1922

The Chinese Open Source Heatmap is live 🔥
You can now track the companies/ research labs/ communities powering China’s open source AI movement.

zh-ai-community/model-release-heatmap-zh

Some highlights:

✨Giant Tech are investing more in open source.
-Alibaba: Full stack open ecosystem
-Tecent: Hunyuan image/video/3D
-Bytedance: Catching up fast in 2025
-Baidu: New player in open LLM

✨New players emerging post–DeepSeek moment.
-Xiaomi
-Red Note
-Bilibili
-MiniMax
-Moonshot AI

✨Startup list is shifting fast! Those who find a direction aligned with their strengths are the ones who endure.
-DeepSeek
-MiniMax
-StepFun
-Moonshot AI
-Zhipu AI
-OpenBMB

✨Research Lab & Community are making key contributions.
-BAAI
-Shanghai AI Lab
-OpenMOSS
-MAP

reacted to stefan-french's post with 👍 6 days ago

Post

2150

🚀 We just released the WASM Agent Blueprint!

It shows how to run Python-based AI agents directly in your browser using WebAssembly (WASM) via Pyodide and the OpenAI Agents SDK. There are no installs, it runs straight in your browser.

Try it out and explore the code 👉 https://github.com/mozilla-ai/wasm-agents-blueprint

1 reply

reacted to bartowski's post with 👍🤗 10 days ago

Post

9841

Was going to post this on /r/LocalLLaMa, but apparently it's without moderation at this time :')

bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF

Was able to use previous mistral chat templates, some hints from Qwen templates, and Claude to piece together a seemingly working chat template, tested it with llama.cpp server and got perfect results, though lmstudio still seems to be struggling for some reason (don't know how to specify a jinja file there)

Outlined the details of the script and results in my llama.cpp PR to add the jinja template:

https://github.com/ggml-org/llama.cpp/pull/14349

Start server with a command like this:

./llama-server -m /models/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q4_K_M.gguf --jinja --chat-template-file /models/Mistral-Small-3.2-24B-Instruct-2506.jinja

and it should be perfect! Hoping it'll work for ALL tools if lmstudio gets an update or something, not just llama.cpp, but very happy to see it works flawlessly in llama.cpp

In the meantime, will try to open a PR to minja to make the strftime work, but no promises :)

reacted to Kseniase's post with 🤗 12 days ago

Post

3445

10 Open-source Deep Research assistants

Deep Research agents are quickly becoming our daily co-workers — built for complex investigations, not just chat. With modular architecture, advanced tool use and real web access, they go far beyond typical AI. While big-name agents get the spotlight, we want to highlight some powerful recent open-source alternatives:

1. DeerFlow -> https://github.com/bytedance/deer-flow
A modular multi-agent system combining LMs and tools for automated research and code analysis. It links a coordinator, planner, team of specialized agent, and reporter, and converts reports to speech via Text-to-Speech (TTS)

2. Alita -> https://github.com/CharlesQ9/Alita
Uses a single problem-solving module for scalable reasoning through simplicity. It self-evolves by generating and reusing Model Context Protocols (MCPs) from open-source tools to build external capabilities for diverse tasks

3. WebThinker -> https://github.com/RUC-NLPIR/WebThinker
Lets reasoning models autonomously search the web and navigate pages. Deep Web Explorer allows interaction with links and follow-up searches. Through a Think-Search-and-Draft process models generate and refine reports in real time. RL training with preference pairs improves the workflow

4. SimpleDeepSearcher -> https://github.com/RUCAIBox/SimpleDeepSearcher
A lightweight framework showing that supervised fine-tuning is a real alternative to complex RL, using simulated web interactions and multi-criteria curation to generate high-quality training data

5. AgenticSeek -> https://github.com/Fosowl/agenticSeek
A private, on-device assistant that picks the best agent expert for browsing, coding, or planning—no cloud needed. Includes voice input via speech-to-text

6. Suna -> https://github.com/kortix-ai/suna
Offers web browsing, file and doc handling, CLI execution, site deployment, and API/service integration—all in one assistant

Subscribe to the Turing Post:https://www.turingpost.com/subscribe
Read further ⬇️

2 replies

reacted to YerbaPage's post with 🤗👀 12 days ago

Post

2984

Curated list of **Next Gen Code Generation** papers & benchmarks! 🔥 with 100+ ⭐️ now!

Stay ahead with the latest in:
✅ Repo-level Issue Resolution (SWE-bench, Agents)
✅ Repo-level Code Completion (Repo understanding)
✅ Repo-level Code QA/Translation
✅ Datasets & Benchmarks

👉 Check it out: https://github.com/YerbaPage/Awesome-Repo-Level-Code-Generation 🔥
💡PRs are welcomed!

posted an update 12 days ago

Post

1696

Model pruning for the masses!

As of version [5740](https://github.com/ggml-org/llama.cpp/releases/tag/b5740), llama-quantize now supports layer pruning via the --prune-layers flag!

Findings so far are that removing one or two layers has a relatively moderate impact on quality. PPL and KLD suffer quite a lot, as expected considering that pruning changes the logits distribution, but the drop in inference quality, as reflected by tests' scores, is less pronounced.

For example, using the Q4_K_M variants as a benchmark, the average drop between eaddario/gemma-3-12b-it-pruned-GGUF and eaddario/gemma-3-12b-it-GGUF is < 3% (60.03 vs 61.65). Similar behaviour for eaddario/Qwen3-30B-A3B-pruned-GGUF and eaddario/Qwen3-30B-A3B-GGUF, albeit with a bit higher impact at ~5.5% (54.19 vs 57.36).

These results seem to confirm Xin Men's et al ShortGPT: Layers in Large Language Models are More Redundant Than You Expect (2403.03853)

Another interesting side-effect, at least with Qwen3-30B-A3B, is that pruning 3 or more layers makes the model forget English and reply in Chinese! but with still reasonable answers.

1 reply

posted an update 13 days ago

Post

3720

Layer-wise and Pruned versions of Qwen/Qwen3-30B-A3B

* Tesor-wise: eaddario/Qwen3-30B-A3B-GGUF
* Pruned: eaddario/Qwen3-30B-A3B-pruned-GGUF

Even though the Perplexity scores of the pruned version are 3 times higher, the ARC, HellaSwag, MMLU, Truthful QA and WinoGrande scores are holding remarkably well, considering two layers were removed (5 and 39). This seems to support Xin Men et al conclusions in
ShortGPT: Layers in Large Language Models are More Redundant Than You Expect (2403.03853)

Results summary in the model's card and test results in the ./scores directory. Questions/feedback is always welcomed.

reacted to AdinaY's post with 👍 15 days ago

Post

1618

LongWriter-Zero 🔥 A Purely RL trained LLM handles 10K+ token coherent passages by Tsinghua University

Model:
THU-KEG/LongWriter-Zero-32B
Dataset:
THU-KEG/LongWriter-Zero-RLData
Paper:
LongWriter-Zero: Mastering Ultra-Long Text Generation via Reinforcement Learning (2506.18841)

✨ 32B
✨ Multi-reward GRPO: length, fluency, structure, non-redundancy
✨ Enforces <think><answer> format via Format RM
✨ Build on Qwen2.5-32B-base

replied to their post about 1 month ago

Thanks @ubergarm . As usual, very insightful!

reacted to AdinaY's post with 👍 about 1 month ago

Post

1029

AReaL-boba² 🔥 A fully async RL system by Ant Research & Tsinghua.

Paper: AReaL: A Large-Scale Asynchronous Reinforcement Learning System for Language Reasoning (2505.24298)
Model:
inclusionAI/areal-boba-2-683f0e819ccb7bb2e1b2f2d5

✨ 8B/14B/32B models, datasets & paper – all on the hub
✨ 2.77× faster training
✨ Native Agentic RL support

reacted to danieldk's post with 🤗 about 1 month ago

Post

1703

We have been working on a project called kernels. kernels makes it possible to load compute kernels directly from the Hub! 🚀

We plan to give kernels a more proper introduction soon. But for those who have been following along, we are happy to announce a new release:

- New layer API with torch.compile support.
- Experimental support for loading Apple Silicon Metal 🤘 Kernels.
- Generate wheels from Hub kernels for legacy deployments.

Full release notes here: https://github.com/huggingface/kernels/releases/tag/v0.6.0

reacted to MJannik's post with 🤝 about 1 month ago

Post

1617

Hi everyone, we’ve got big news! Starting today, all Langfuse product features are available as free OSS (MIT license).

You can now upgrade your self-hosted Langfuse to access features like:
- Managed LLM-as-a-Judge evaluations
- Annotation queues
- Prompt experiments
- LLM playground

We’re incredibly grateful for the support of this amazing community and can’t wait to hear your feedback on the new features!

More on this change here: https://langfuse.com/blog/2025-06-04-open-sourcing-langfuse-product

posted an update about 1 month ago

Post

1312

Layer-wise and Pruned versions of google/gemma-3-12b-it

After enhancing llama.cpp to handle user-defined quantization levels for arbitrary tensors (https://github.com/ggml-org/llama.cpp/pull/12511), I have added an option to prune whole layers (https://github.com/ggml-org/llama.cpp/pull/13037), and have published two versions of google/gemma-3-12b-it for demo and testing purposes:

* Tesor-wise: eaddario/gemma-3-12b-it-GGUF
* Pruned: eaddario/gemma-3-12b-it-pruned-GGUF

Even though the Perplexity scores of the pruned version are 3 times higher, the ARC, HellaSwag, MMLU, Truthful QA and WinoGrande scores are holding remarkably well, considering two layers were removed (26 and 29). This seems to support Xin Men et al conclusions in ShortGPT: Layers in Large Language Models are More Redundant Than You Expect (2403.03853)

Results summary in the model's card and test results in the ./scores directory. Questions/feedback is always welcomed.

posted an update about 2 months ago

Post

381

HF community survey: What is an acceptable Perplexity (PPL) degradation?

An area of personal research is to find ways to shrink the size of LLMs without incurring in a noticeable loss of capability. All the models in my repo have been generated by quantizing different tensors at different levels based on how much they influence the inference process (see the model's card for more details). This approach produces, on average, a ~10% size reduction with < 1% of PPL penalty.

I'm now focusing on pruning (whole layer removal), as a way to achieve better size reduction, but this comes at the cost of a much higher PPL degradation.

So, the question for the HF community is: what is the lowest/worst PPL correlation coefficient (𝜌PPL) you'd consider acceptable for a quantized model? (e.g. 99%? 95%? 90%? etc)

To clarify, by 𝜌PPL I mean the Cor(ln(PPL(Q)), ln(PPL(base))) statistic generated by llama-perplexity.

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