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.
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.
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
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-7.1K
24-hour runs
-12.4K
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-12.4K
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-12.4K
30-day runs
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