Towards Best Practices for Open Datasets for LLM Training
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2501.08365
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Published
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39
Need4Speed
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lvwerra
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a
paper
2 days ago
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2323
Are we the only providers of INT4 quantized models for Llama 3.2 VL?
OPEA/Llama-3.2-90B-Vision-Instruct-int4-sym-inc
OPEA/Llama-3.2-11B-Vision-Instruct-int4-sym-inc
OPEA/Llama-3.2-90B-Vision-Instruct-int4-sym-inc
OPEA/Llama-3.2-11B-Vision-Instruct-int4-sym-inc
- 3 replies
Post
1810
AutoRound has demonstrated strong results even at 2-bit precision for VLM models like QWEN2-VL-72B. Check it out here:
OPEA/Qwen2-VL-72B-Instruct-int2-sym-inc.
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- 4 replies
Post
339
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
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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- 3 replies
Haihao
authored
a
paper
about 1 month ago
Post
983
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
Llama-3.2-11B-Vision-Instruct, Qwen2.5, Llama3.1, etc. Check out https://huggingface.co/OPEA
Post
1905
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?
- 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?
ofirzaf
authored
2
papers
about 2 months ago
orenpereg
authored
5
papers
2 months ago
Term Set Expansion based on Multi-Context Term Embeddings: an End-to-end Workflow
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1807.10104
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Published
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1
ABSApp: A Portable Weakly-Supervised Aspect-Based Sentiment Extraction System
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1909.05608
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Published
Cross-Domain Aspect Extraction using Transformers Augmented with Knowledge Graphs
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2210.10144
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Published
Efficient Few-Shot Learning Without Prompts
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2209.11055
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Published
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3
Accelerating Speculative Decoding using Dynamic Speculation Length
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2405.04304
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Published
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2
lvwerra
authored
a
paper
3 months ago
Haihao
authored
3
papers
3 months ago
moshew
authored
a
paper
5 months ago
Post
651
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.