casperhansen / llama-3.3-70b-instruct-awq

huggingface.co
Total runs: 809.8K
24-hour runs: -7.1K
7-day runs: -12.4K
30-day runs: -12.4K
Model's Last Updated: December 07 2024
text-generation

Introduction of llama-3.3-70b-instruct-awq

Model Details of llama-3.3-70b-instruct-awq

This is the AWQ version of the Llama 3.3 70B Instruct model. Find more info here: https://github.com/casper-hansen/AutoAWQ .

Model Information

The Meta Llama 3.3 multilingual large language model (LLM) is a pretrained and instruction tuned generative model in 70B (text in/text out). The Llama 3.3 instruction tuned text only model is optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.

Model developer : Meta

Model Architecture: Llama 3.3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.

Training Data Params Input modalities Output modalities Context length GQA Token count Knowledge cutoff
Llama 3.3 (text only) A new mix of publicly available online data. 70B Multilingual Text Multilingual Text and code 128k Yes 15T+ December 2023

Supported languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.

Llama 3.3 model . Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability.

Model Release Date:

  • 70B Instruct: December 6, 2024

Status: This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.

License A custom commercial license, the Llama 3.3 Community License Agreement, is available at: https://github.com/meta-llama/llama-models/blob/main/models/llama3_3/LICENSE

Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model README . For more technical information about generation parameters and recipes for how to use Llama 3.3 in applications, please go here .

Benchmark
Category Benchmark # Shots Metric Llama 3.1 8B Instruct Llama 3.1 70B Instruct Llama-3.3 70B Instruct Llama 3.1 405B Instruct
MMLU (CoT) 0 macro_avg/acc 73.0 86.0 86.0 88.6
MMLU Pro (CoT) 5 macro_avg/acc 48.3 66.4 68.9 73.3
Steerability IFEval 80.4 87.5 92.1 88.6
Reasoning GPQA Diamond (CoT) 0 acc 31.8 48.0 50.5 49.0
Code HumanEval 0 pass@1 72.6 80.5 88.4 89.0
MBPP EvalPlus (base) 0 pass@1 72.8 86.0 87.6 88.6
Math MATH (CoT) 0 sympy_intersection_score 51.9 68.0 77.0 73.8
Tool Use BFCL v2 0 overall_ast_summary/macro_avg/valid 65.4 77.5 77.3 81.1
Multilingual MGSM 0 em 68.9 86.9 91.1 91.6

Runs of casperhansen llama-3.3-70b-instruct-awq on huggingface.co

809.8K
Total runs
-7.1K
24-hour runs
-12.4K
3-day runs
-12.4K
7-day runs
-12.4K
30-day runs

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