Need4Speed

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wenhuach 
posted an update 3 days ago
wenhuach 
posted an update 9 days ago
wenhuach 
posted an update 20 days ago
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329
This week, OPEA Space released several new INT4 models, including:
nvidia/Llama-3.1-Nemotron-70B-Instruct-HF
allenai/OLMo-2-1124-13B-Instruct
THUDM/glm-4v-9b
AIDC-AI/Marco-o1
and several others.
Let us know which models you'd like prioritized for quantization, and we'll do our best to make it happen!

https://huggingface.co/OPEA
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wenhuach 
posted an update 27 days ago
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977
OPEA space just releases nearly 20 int4 models, for example, QWQ-32B-Preview,
Llama-3.2-11B-Vision-Instruct, Qwen2.5, Llama3.1, etc. Check out https://huggingface.co/OPEA
loubnabnl 
posted an update about 1 month ago
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1639
Making SmolLM2 reproducible: open-sourcing our training & evaluation toolkit 🛠️ https://github.com/huggingface/smollm/

- Pre-training code with nanotron
- Evaluation suite with lighteval
- Synthetic data generation using distilabel (powers our new SFT dataset HuggingFaceTB/smoltalk)
- Post-training scripts with TRL & the alignment handbook
- On-device tools with llama.cpp for summarization, rewriting & agents

Apache 2.0 licensed. V2 pre-training data mix coming soon!

Which other tools should we add next?
wenhuach 
posted an update 5 months ago
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649
Try to find a better int4 algorithm for LLAMA3.1? For the 8B model, AutoRound boasts an average improvement across 10 zero-shot tasks, scoring 63.93 versus 63.15 (AWQ). Notably, on the MMLU task, it achieved 66.72 compared to 65.25, and on the ARC-C task, it scored 52.13 against 50.94. For further details and comparisons, visit the leaderboard at Intel/low_bit_open_llm_leaderboard.