casperhansen/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