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--- |
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tags: |
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- w8a8 |
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- int8 |
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- vllm |
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license: apache-2.0 |
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license_link: https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md |
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language: |
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- en |
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base_model: ibm-granite/granite-3.1-8b-instruct |
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library_name: transformers |
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--- |
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# granite-3.1-8b-instruct-quantized.w8a8 |
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## Model Overview |
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- **Model Architecture:** granite-3.1-8b-instruct |
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- **Input:** Text |
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- **Output:** Text |
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- **Model Optimizations:** |
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- **Weight quantization:** INT8 |
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- **Activation quantization:** INT8 |
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- **Release Date:** 1/8/2025 |
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- **Version:** 1.0 |
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- **Model Developers:** Neural Magic |
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Quantized version of [ibm-granite/granite-3.1-8b-instruct](https://huggingface.co/ibm-granite/granite-3.1-8b-instruct). |
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It achieves an average score of 70.26 on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves 70.30. |
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### Model Optimizations |
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This model was obtained by quantizing the weights and activations of [ibm-granite/granite-3.1-8b-instruct](https://huggingface.co/ibm-granite/granite-3.1-8b-instruct) to INT8 data type, ready for inference with vLLM >= 0.5.2. |
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This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. Only the weights and activations of the linear operators within transformers blocks are quantized. |
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## Deployment |
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### Use with vLLM |
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This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below. |
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```python |
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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/granite-3.1-8b-instruct-quantized.w8a8" |
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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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messages_list = [ |
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[{"role": "user", "content": "Who are you? Please respond in pirate speak!"}], |
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] |
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prompt_token_ids = [tokenizer.apply_chat_template(messages, add_generation_prompt=True) for messages in messages_list] |
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outputs = llm.generate(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params) |
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generated_text = [output.outputs[0].text for output in outputs] |
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print(generated_text) |
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``` |
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vLLM also supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details. |
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## Creation |
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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_path ibm-granite/granite-3.1-8b-instruct --quant_path "output_dir/granite-3.1-8b-instruct-quantized.w8a8" --calib_size 3072 --dampening_frac 0.1 --observer mse |
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``` |
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```python |
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from datasets import load_dataset |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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from llmcompressor.modifiers.quantization import GPTQModifier |
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from llmcompressor.modifiers.smoothquant import SmoothQuantModifier |
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from llmcompressor.transformers import oneshot, apply |
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import argparse |
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from compressed_tensors.quantization import QuantizationScheme, QuantizationArgs, QuantizationType, QuantizationStrategy |
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parser = argparse.ArgumentParser() |
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parser.add_argument('--model_path', type=str) |
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parser.add_argument('--quant_path', type=str) |
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parser.add_argument('--calib_size', type=int, default=256) |
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parser.add_argument('--dampening_frac', type=float, default=0.1) |
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parser.add_argument('--observer', type=str, default="minmax") |
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args = parser.parse_args() |
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model = AutoModelForCausalLM.from_pretrained( |
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args.model_path, |
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device_map="auto", |
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torch_dtype="auto", |
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use_cache=False, |
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trust_remote_code=True, |
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) |
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tokenizer = AutoTokenizer.from_pretrained(args.model_path) |
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NUM_CALIBRATION_SAMPLES = args.calib_size |
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DATASET_ID = "neuralmagic/LLM_compression_calibration" |
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DATASET_SPLIT = "train" |
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ds = load_dataset(DATASET_ID, split=DATASET_SPLIT) |
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ds = ds.shuffle(seed=42).select(range(NUM_CALIBRATION_SAMPLES)) |
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def preprocess(example): |
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concat_txt = example["instruction"] + "\n" + example["output"] |
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return {"text": concat_txt} |
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ds = ds.map(preprocess) |
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def tokenize(sample): |
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return tokenizer( |
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sample["text"], |
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padding=False, |
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truncation=False, |
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add_special_tokens=True, |
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) |
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ds = ds.map(tokenize, remove_columns=ds.column_names) |
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ignore=["lm_head"] |
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mappings=[ |
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[["re:.*q_proj", "re:.*k_proj", "re:.*v_proj"], "re:.*input_layernorm"], |
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[["re:.*gate_proj", "re:.*up_proj"], "re:.*post_attention_layernorm"], |
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[["re:.*down_proj"], "re:.*up_proj"] |
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] |
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recipe = [ |
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SmoothQuantModifier(smoothing_strength=0.8, ignore=ignore, mappings=mappings), |
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GPTQModifier( |
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targets=["Linear"], |
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ignore=["lm_head"], |
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scheme="W8A8", |
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dampening_frac=args.dampening_frac, |
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observer=args.observer, |
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) |
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] |
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oneshot( |
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model=model, |
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dataset=ds, |
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recipe=recipe, |
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num_calibration_samples=args.calib_size, |
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max_seq_length=8196, |
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) |
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# Save to disk compressed. |
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model.save_pretrained(quant_path, save_compressed=True) |
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tokenizer.save_pretrained(quant_path) |
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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-instruct-quantized.w8a8",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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--output_path output_dir \ |
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--show_config |
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``` |
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OpenLLM Leaderboard V2: |
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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-instruct-quantized.w8a8",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 leaderboard \ |
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--write_out \ |
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--batch_size auto \ |
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--output_path output_dir \ |
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--show_config |
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``` |
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#### HumanEval |
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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-instruct-quantized.w8a8 \ |
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--bs 16 \ |
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--temperature 0.2 \ |
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--n_samples 50 \ |
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--root "." \ |
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--dataset humaneval |
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``` |
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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-instruct-quantized.w8a8_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-instruct-quantized.w8a8_vllm_temp_0.2-sanitized |
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``` |
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</details> |
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### Accuracy |
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<table> |
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<thead> |
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<tr> |
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<th>Category</th> |
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<th>Metric</th> |
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<th>ibm-granite/granite-3.1-8b-instruct</th> |
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<th>neuralmagic/granite-3.1-8b-instruct-quantized.w8a8</th> |
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<th>Recovery (%)</th> |
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</tr> |
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</thead> |
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<tbody> |
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<!-- OpenLLM Leaderboard V1 --> |
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<tr> |
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<td rowspan="7"><b>OpenLLM V1</b></td> |
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<td>ARC-Challenge (Acc-Norm, 25-shot)</td> |
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<td>66.81</td> |
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<td>67.06</td> |
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<td>100.37</td> |
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</tr> |
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<tr> |
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<td>GSM8K (Strict-Match, 5-shot)</td> |
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<td>64.52</td> |
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<td>65.66</td> |
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<td>101.77</td> |
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</tr> |
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<tr> |
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<td>HellaSwag (Acc-Norm, 10-shot)</td> |
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<td>84.18</td> |
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<td>83.93</td> |
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<td>99.70</td> |
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</tr> |
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<tr> |
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<td>MMLU (Acc, 5-shot)</td> |
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<td>65.52</td> |
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<td>65.03</td> |
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<td>99.25</td> |
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</tr> |
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<tr> |
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<td>TruthfulQA (MC2, 0-shot)</td> |
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<td>60.57</td> |
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<td>60.02</td> |
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<td>99.09</td> |
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</tr> |
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<tr> |
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<td>Winogrande (Acc, 5-shot)</td> |
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<td>80.19</td> |
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<td>79.87</td> |
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<td>99.60</td> |
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</tr> |
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<tr> |
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<td><b>Average Score</b></td> |
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<td><b>70.30</b></td> |
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<td><b>70.26</b></td> |
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<td><b>99.95</b></td> |
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</tr> |
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<!-- OpenLLM Leaderboard V2 --> |
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<tr> |
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<td rowspan="7"><b>OpenLLM V2</b></td> |
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<td>IFEval (Inst Level Strict Acc, 0-shot)</td> |
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<td>74.01</td> |
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<td>73.50</td> |
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<td>99.31</td> |
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</tr> |
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<tr> |
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<td>BBH (Acc-Norm, 3-shot)</td> |
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<td>53.19</td> |
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<td>52.59</td> |
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<td>98.87</td> |
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</tr> |
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<tr> |
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<td>Math-Hard (Exact-Match, 4-shot)</td> |
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<td>14.77</td> |
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<td>15.73</td> |
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<td>106.50</td> |
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</tr> |
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<tr> |
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<td>GPQA (Acc-Norm, 0-shot)</td> |
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<td>31.76</td> |
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<td>30.62</td> |
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<td>96.40</td> |
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</tr> |
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<tr> |
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<td>MUSR (Acc-Norm, 0-shot)</td> |
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<td>46.01</td> |
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<td>44.30</td> |
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<td>96.28</td> |
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</tr> |
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<tr> |
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<td>MMLU-Pro (Acc, 5-shot)</td> |
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<td>35.81</td> |
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<td>35.41</td> |
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<td>98.88</td> |
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</tr> |
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<tr> |
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<td><b>Average Score</b></td> |
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<td><b>42.61</b></td> |
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<td><b>42.03</b></td> |
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<td><b>98.64</b></td> |
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</tr> |
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<!-- HumanEval --> |
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<tr> |
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<td rowspan="2"><b>Coding</b></td> |
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<td>HumanEval Pass@1</td> |
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<td>71.00</td> |
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<td>70.50</td> |
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<td><b>99.30</b></td> |
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</tr> |
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</tbody> |
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</table> |
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## Inference Performance |
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This model achieves up to 1.6x speedup in single-stream deployment and up to 1.7x speedup in multi-stream asynchronous deployment, depending on hardware and use-case scenario. |
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The following performance benchmarks were conducted with [vLLM](https://docs.vllm.ai/en/latest/) version 0.6.6.post1, and [GuideLLM](https://github.com/neuralmagic/guidellm). |
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<details> |
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<summary>Benchmarking Command</summary> |
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``` |
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guidellm --model neuralmagic/granite-3.1-8b-instruct-quantized.w8a8 --target "http://localhost:8000/v1" --data-type emulated --data "prompt_tokens=<prompt_tokens>,generated_tokens=<generated_tokens>" --max seconds 360 --backend aiohttp_server |
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``` |
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</details> |
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### Single-stream performance (measured with vLLM version 0.6.6.post1) |
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<table> |
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<tr> |
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<td></td> |
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<td></td> |
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<td></td> |
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<th style="text-align: center;" colspan="7" >Latency (s)</th> |
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</tr> |
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<tr> |
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<th>GPU class</th> |
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<th>Model</th> |
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<th>Speedup</th> |
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<th>Code Completion<br>prefill: 256 tokens<br>decode: 1024 tokens</th> |
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<th>Docstring Generation<br>prefill: 768 tokens<br>decode: 128 tokens</th> |
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<th>Code Fixing<br>prefill: 1024 tokens<br>decode: 1024 tokens</th> |
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<th>RAG<br>prefill: 1024 tokens<br>decode: 128 tokens</th> |
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<th>Instruction Following<br>prefill: 256 tokens<br>decode: 128 tokens</th> |
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<th>Multi-turn Chat<br>prefill: 512 tokens<br>decode: 256 tokens</th> |
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<th>Large Summarization<br>prefill: 4096 tokens<br>decode: 512 tokens</th> |
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</tr> |
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<tr> |
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<td style="vertical-align: middle;" rowspan="3" >A5000</td> |
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<td>granite-3.1-8b-instruct</td> |
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<td></td> |
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<td>28.3</td> |
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<td>3.7</td> |
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<td>28.8</td> |
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<td>3.8</td> |
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<td>3.6</td> |
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<td>7.2</td> |
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<td>15.7</td> |
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</tr> |
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<tr> |
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<td>granite-3.1-8b-instruct-quantized.w8a8<br>(this model)</td> |
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<td>1.60</td> |
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<td>17.7</td> |
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<td>2.3</td> |
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<td>18.0</td> |
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<td>2.4</td> |
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<td>2.2</td> |
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<td>4.5</td> |
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<td>10.0</td> |
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</tr> |
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<tr> |
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<td>granite-3.1-8b-instruct-quantized.w4a16</td> |
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<td>2.61</td> |
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<td>10.3</td> |
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<td>1.5</td> |
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<td>10.7</td> |
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<td>1.5</td> |
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<td>1.3</td> |
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<td>2.7</td> |
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<td>6.6</td> |
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</tr> |
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<tr> |
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<td style="vertical-align: middle;" rowspan="3" >A6000</td> |
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<td>granite-3.1-8b-instruct</td> |
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<td></td> |
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<td>25.8</td> |
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<td>3.4</td> |
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<td>26.2</td> |
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<td>3.4</td> |
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<td>3.3</td> |
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<td>6.5</td> |
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<td>14.2</td> |
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</tr> |
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<tr> |
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<td>granite-3.1-8b-instruct-quantized.w8a8<br>(this model)</td> |
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<td>1.50</td> |
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<td>17.4</td> |
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<td>2.3</td> |
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<td>16.9</td> |
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<td>2.2</td> |
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<td>2.2</td> |
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<td>4.4</td> |
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<td>9.8</td> |
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</tr> |
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<tr> |
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<td>granite-3.1-8b-instruct-quantized.w4a16</td> |
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<td>2.48</td> |
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<td>10.0</td> |
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<td>1.4</td> |
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<td>10.4</td> |
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<td>1.5</td> |
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<td>1.3</td> |
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<td>2.5</td> |
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<td>6.2</td> |
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</tr> |
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<tr> |
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<td style="vertical-align: middle;" rowspan="3" >A100</td> |
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<td>granite-3.1-8b-instruct</td> |
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<td></td> |
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<td>13.6</td> |
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<td>1.8</td> |
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<td>13.7</td> |
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<td>1.8</td> |
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<td>1.7</td> |
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<td>3.4</td> |
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<td>7.3</td> |
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</tr> |
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<tr> |
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<td>granite-3.1-8b-instruct-quantized.w8a8<br>(this model)</td> |
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<td>1.31</td> |
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<td>10.4</td> |
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<td>1.3</td> |
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<td>10.5</td> |
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<td>1.4</td> |
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<td>1.3</td> |
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<td>2.6</td> |
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<td>5.6</td> |
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</tr> |
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<tr> |
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<td>granite-3.1-8b-instruct-quantized.w4a16</td> |
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<td>1.80</td> |
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<td>7.3</td> |
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<td>1.0</td> |
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<td>7.4</td> |
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<td>1.0</td> |
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<td>0.9</td> |
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<td>1.9</td> |
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<td>4.3</td> |
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</tr> |
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</table> |
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|
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### Multi-stream asynchronous performance (measured with vLLM version 0.6.6.post1) |
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<table> |
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<tr> |
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<td></td> |
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<td></td> |
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<td></td> |
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<th style="text-align: center;" colspan="7" >Maximum Throughput (Queries per Second)</th> |
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</tr> |
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<tr> |
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<th>GPU class</th> |
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<th>Model</th> |
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<th>Speedup</th> |
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<th>Code Completion<br>prefill: 256 tokens<br>decode: 1024 tokens</th> |
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<th>Docstring Generation<br>prefill: 768 tokens<br>decode: 128 tokens</th> |
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<th>Code Fixing<br>prefill: 1024 tokens<br>decode: 1024 tokens</th> |
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<th>RAG<br>prefill: 1024 tokens<br>decode: 128 tokens</th> |
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<th>Instruction Following<br>prefill: 256 tokens<br>decode: 128 tokens</th> |
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<th>Multi-turn Chat<br>prefill: 512 tokens<br>decode: 256 tokens</th> |
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<th>Large Summarization<br>prefill: 4096 tokens<br>decode: 512 tokens</th> |
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</tr> |
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<tr> |
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<td style="vertical-align: middle;" rowspan="3" >A5000</td> |
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<td>granite-3.1-8b-instruct</td> |
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<td></td> |
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<td>0.8</td> |
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<td>3.1</td> |
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<td>0.4</td> |
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<td>2.5</td> |
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<td>6.7</td> |
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<td>2.7</td> |
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<td>0.3</td> |
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</tr> |
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<tr> |
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<td>granite-3.1-8b-instruct-quantized.w8a8<br>(this model)</td> |
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<td>1.71</td> |
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<td>1.3</td> |
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<td>5.2</td> |
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<td>0.9</td> |
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<td>4.0</td> |
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<td>10.5</td> |
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<td>4.4</td> |
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<td>0.5</td> |
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</tr> |
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<tr> |
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<td>granite-3.1-8b-instruct-quantized.w4a16</td> |
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<td>1.46</td> |
|
<td>1.3</td> |
|
<td>3.9</td> |
|
<td>0.8</td> |
|
<td>2.9</td> |
|
<td>8.2</td> |
|
<td>3.6</td> |
|
<td>0.5</td> |
|
</tr> |
|
<tr> |
|
<td style="vertical-align: middle;" rowspan="3" >A6000</td> |
|
<td>granite-3.1-8b-instruct</td> |
|
<td></td> |
|
<td>1.3</td> |
|
<td>5.1</td> |
|
<td>0.9</td> |
|
<td>4.0</td> |
|
<td>0.3</td> |
|
<td>4.3</td> |
|
<td>0.6</td> |
|
</tr> |
|
<tr> |
|
<td>granite-3.1-8b-instruct-quantized.w8a8<br>(this model)</td> |
|
<td>1.39</td> |
|
<td>1.8</td> |
|
<td>7.0</td> |
|
<td>1.3</td> |
|
<td>5.6</td> |
|
<td>14.0</td> |
|
<td>6.3</td> |
|
<td>0.8</td> |
|
</tr> |
|
<tr> |
|
<td>granite-3.1-8b-instruct-quantized.w4a16</td> |
|
<td>1.09</td> |
|
<td>1.9</td> |
|
<td>4.8</td> |
|
<td>1.0</td> |
|
<td>3.8</td> |
|
<td>10.0</td> |
|
<td>5.0</td> |
|
<td>0.6</td> |
|
</tr> |
|
<tr> |
|
<td style="vertical-align: middle;" rowspan="3" >A100</td> |
|
<td>granite-3.1-8b-instruct</td> |
|
<td></td> |
|
<td>3.1</td> |
|
<td>10.7</td> |
|
<td>2.1</td> |
|
<td>8.5</td> |
|
<td>20.6</td> |
|
<td>9.6</td> |
|
<td>1.4</td> |
|
</tr> |
|
<tr> |
|
<td>granite-3.1-8b-instruct-quantized.w8a8<br>(this model)</td> |
|
<td>1.23</td> |
|
<td>3.8</td> |
|
<td>14.2</td> |
|
<td>2.1</td> |
|
<td>11.4</td> |
|
<td>25.9</td> |
|
<td>12.1</td> |
|
<td>1.7</td> |
|
</tr> |
|
<tr> |
|
<td>granite-3.1-8b-instruct-quantized.w4a16</td> |
|
<td>0.96</td> |
|
<td>3.4</td> |
|
<td>9.0</td> |
|
<td>2.6</td> |
|
<td>7.2</td> |
|
<td>18.0</td> |
|
<td>8.8</td> |
|
<td>1.3</td> |
|
</tr> |
|
</table> |
|
|
