Update README.md
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README.md
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@@ -42,7 +42,7 @@ from transformers import AutoTokenizer
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from vllm import LLM, SamplingParams
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max_model_len, tp_size = 4096, 1
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model_name = "neuralmagic
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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llm = LLM(model=model_name, tensor_parallel_size=tp_size, max_model_len=max_model_len, trust_remote_code=True)
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sampling_params = SamplingParams(temperature=0.3, max_tokens=256, stop_token_ids=[tokenizer.eos_token_id])
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This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below.
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```bash
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python quantize.py --model_id ibm-granite/granite-3.1-8b-base --save_path "output_dir/"
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```
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if __name__ == "__main__":
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main()
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```
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## Evaluation
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The model was evaluated on OpenLLM Leaderboard [V1](https://huggingface.co/spaces/open-llm-leaderboard-old/open_llm_leaderboard) and on [HumanEval](https://github.com/neuralmagic/evalplus), using the following commands:
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OpenLLM Leaderboard V1:
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```
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lm_eval \
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--model vllm \
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--model_args pretrained="neuralmagic
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--tasks openllm \
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--write_out \
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--batch_size auto \
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##### Generation
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```
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python3 codegen/generate.py \
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--model neuralmagic
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--bs 16 \
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--temperature 0.2 \
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--n_samples 50 \
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##### Sanitization
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```
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python3 evalplus/sanitize.py \
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humaneval/neuralmagic
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```
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##### Evaluation
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```
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evalplus.evaluate \
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--dataset humaneval \
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--samples humaneval/neuralmagic
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```
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### Accuracy
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#### OpenLLM Leaderboard V1 evaluation scores
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| Metric | ibm-granite/granite-3.1-8b-base | neuralmagic
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|-----------------------------------------|:---------------------------------:|:-------------------------------------------:|
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| ARC-Challenge (Acc-Norm, 25-shot) | 64.68 | 64.16 |
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| GSM8K (Strict-Match, 5-shot) | 60.88 | 58.45 |
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| **Recovery** | **100.00** | **99.26** |
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#### HumanEval pass@1 scores
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| Metric | ibm-granite/granite-3.1-8b-base | neuralmagic
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|-----------------------------------------|:---------------------------------:|:-------------------------------------------:|
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| HumanEval Pass@1 | 44.10 | 44.8 |
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from vllm import LLM, SamplingParams
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max_model_len, tp_size = 4096, 1
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model_name = "neuralmagic/granite-3.1-8b-base-FP8-dynamic"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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llm = LLM(model=model_name, tensor_parallel_size=tp_size, max_model_len=max_model_len, trust_remote_code=True)
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sampling_params = SamplingParams(temperature=0.3, max_tokens=256, stop_token_ids=[tokenizer.eos_token_id])
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This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below.
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<details>
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<summary>Model Creation Code</summary>
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```bash
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python quantize.py --model_id ibm-granite/granite-3.1-8b-base --save_path "output_dir/"
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```
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if __name__ == "__main__":
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main()
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```
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</details>
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## Evaluation
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The model was evaluated on OpenLLM Leaderboard [V1](https://huggingface.co/spaces/open-llm-leaderboard-old/open_llm_leaderboard), OpenLLM Leaderboard [V2](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/) and on [HumanEval](https://github.com/neuralmagic/evalplus), using the following commands:
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<details>
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<summary>Evaluation Commands</summary>
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OpenLLM Leaderboard V1:
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```
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lm_eval \
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--model vllm \
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--model_args pretrained="neuralmagic/granite-3.1-8b-base-FP8-dynamic",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1,gpu_memory_utilization=0.8,enable_chunked_prefill=True,trust_remote_code=True \
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--tasks openllm \
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--write_out \
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--batch_size auto \
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##### Generation
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```
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python3 codegen/generate.py \
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--model neuralmagic/granite-3.1-8b-base-FP8-dynamic \
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--bs 16 \
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--temperature 0.2 \
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--n_samples 50 \
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##### Sanitization
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```
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python3 evalplus/sanitize.py \
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humaneval/neuralmagic--granite-3.1-8b-base-FP8-dynamic_vllm_temp_0.2
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```
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##### Evaluation
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```
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evalplus.evaluate \
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--dataset humaneval \
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--samples humaneval/neuralmagic--granite-3.1-8b-base-FP8-dynamic_vllm_temp_0.2-sanitized
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```
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</details>
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### Accuracy
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#### OpenLLM Leaderboard V1 evaluation scores
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| Metric | ibm-granite/granite-3.1-8b-base | neuralmagic/granite-3.1-8b-base-FP8-dynamic |
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|-----------------------------------------|:---------------------------------:|:-------------------------------------------:|
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| ARC-Challenge (Acc-Norm, 25-shot) | 64.68 | 64.16 |
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| GSM8K (Strict-Match, 5-shot) | 60.88 | 58.45 |
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| **Recovery** | **100.00** | **99.26** |
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#### HumanEval pass@1 scores
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| Metric | ibm-granite/granite-3.1-8b-base | neuralmagic/granite-3.1-8b-base-FP8-dynamic |
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|-----------------------------------------|:---------------------------------:|:-------------------------------------------:|
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| HumanEval Pass@1 | 44.10 | 44.8 |
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