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README.md
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## Evaluation
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The model was evaluated
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<details>
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<summary>Evaluation Commands</summary>
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```
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```
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</details>
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### Accuracy
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<table>
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## Evaluation
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The model was evaluated using [mistral-evals](https://github.com/neuralmagic/mistral-evals) for vision-related tasks and using [lm_evaluation_harness](https://github.com/neuralmagic/lm-evaluation-harness) for select text-based benchmarks. The evaluations were conducted using the following commands:
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<details>
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<summary>Evaluation Commands</summary>
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### Vision Tasks
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- vqav2
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- docvqa
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- mathvista
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- mmmu
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- chartqa
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```
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vllm serve neuralmagic/pixtral-12b-quantized.w8a8 --tensor_parallel_size 1 --max_model_len 25000 --trust_remote_code --max_num_seqs 8 --gpu_memory_utilization 0.9 --dtype float16 --limit_mm_per_prompt image=7
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python -m eval.run eval_vllm \
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--model_name neuralmagic/pixtral-12b-quantized.w8a8 \
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--url http://0.0.0.0:8000 \
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--output_dir ~/tmp \
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--eval_name <vision_task_name>
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```
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### Text-based Tasks
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#### MMLU
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```
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lm_eval \
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--model vllm \
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--model_args pretrained="<model_name>",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=<n>,gpu_memory_utilization=0.8,enable_chunked_prefill=True,trust_remote_code=True \
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--tasks mmlu \
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--num_fewshot 5 \
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--batch_size auto \
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--output_path output_dir
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```
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#### MGSM
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```
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lm_eval \
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--model vllm \
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--model_args pretrained="<model_name>",dtype=auto,max_model_len=4096,max_gen_toks=2048,max_num_seqs=128,tensor_parallel_size=<n>,gpu_memory_utilization=0.9 \
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--tasks mgsm_cot_native \
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--num_fewshot 0 \
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--batch_size auto \
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--output_path output_dir
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```
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</details>
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### Accuracy
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<table>
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