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Model: neuralmagic/granite-3.1-2b-base-quantized.w8a8 Source: Original Platform
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README.md
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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-3.1-2b-base
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library_name: transformers
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---
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# granite-3.1-2b-base-quantized.w8a8
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## Model Overview
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- **Model Architecture:** granite-3.1-2b-base
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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-3.1-2b-base](https://huggingface.co/ibm-granite/granite-3.1-2b-base).
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It achieves an average score of 57.22 on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves 57.65.
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### Model Optimizations
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This model was obtained by quantizing the weights and activations of [ibm-granite/granite-3.1-2b-base](https://huggingface.co/ibm-granite/granite-3.1-2b-base) 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-3.1-2b-base-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-3.1-2b-base --quant_path "output_dir/granite-3.1-2b-base-quantized.w8a8" --calib_size 1024 --dampening_frac 0.01 --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
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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 SparseAutoModelForCausalLM, 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 = SparseAutoModelForCausalLM.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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return {"text": example["text"]}
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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=0.7, 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-3.1-2b-base-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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#### 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-3.1-2b-base-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-3.1-2b-base-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-3.1-2b-base-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-3.1-2b-base</th>
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<th>neuralmagic/granite-3.1-2b-base-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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<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>53.75</td>
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<td>54.01</td>
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<td>100.48</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>47.84</td>
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<td>46.55</td>
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<td>97.30</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>77.94</td>
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<td>77.94</td>
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<td>100.00</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>52.88</td>
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<td>52.34</td>
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<td>98.98</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>39.04</td>
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<td>38.12</td>
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<td>97.64</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>74.43</td>
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<td>74.35</td>
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<td>99.89</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>57.65</b></td>
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<td><b>57.22</b></td>
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<td><b>99.26</b></td>
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</tr>
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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>30.00</td>
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<td>29.60</td>
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<td><b>98.67</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 1.4x speedup in single-stream deployment and up to 1.1x 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-3.1-2b-base-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-3.1-2b-base</td>
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<td></td>
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<td>10.9</td>
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<td>1.4</td>
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<td>11.0</td>
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<td>1.5</td>
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<td>1.4</td>
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<td>2.8</td>
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<td>6.1</td>
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</tr>
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<tr>
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<td>granite-3.1-2b-base-quantized.w8a8<br>(this model)</td>
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<td>1.37</td>
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<td>7.9</td>
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<td>1.0</td>
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<td>8.0</td>
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<td>1.1</td>
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<td>1.0</td>
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<td>2.0</td>
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<td>4.7</td>
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</tr>
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<tr>
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<td>granite-3.1-2b-base-quantized.w4a16</td>
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<td>1.94</td>
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<td>5.4</td>
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<td>0.7</td>
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<td>5.5</td>
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<td>0.8</td>
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<td>0.7</td>
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<td>1.4</td>
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<td>3.4</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-3.1-2b-base</td>
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<td></td>
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<td>9.8</td>
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<td>1.3</td>
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<td>10.0</td>
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<td>1.3</td>
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<td>1.3</td>
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<td>2.6</td>
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<td>5.4</td>
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</tr>
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<tr>
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<td>granite-3.1-2b-base-quantized.w8a8<br>(this model)</td>
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<td>1.31</td>
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<td>7.8</td>
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<td>1.0</td>
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||||
<td>7.6</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>4.5</td>
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</tr>
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<tr>
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<td>granite-3.1-2b-base-quantized.w4a16</td>
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<td>1.87</td>
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<td>5.1</td>
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<td>0.7</td>
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<td>5.2</td>
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||||
<td>0.7</td>
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<td>0.7</td>
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<td>1.3</td>
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<td>3.1</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)
|
||||
<table>
|
||||
<tr>
|
||||
<td></td>
|
||||
<td></td>
|
||||
<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>
|
||||
<th>Model</th>
|
||||
<th>Speedup</th>
|
||||
<th>Code Completion<br>prefill: 256 tokens<br>decode: 1024 tokens</th>
|
||||
<th>Docstring Generation<br>prefill: 768 tokens<br>decode: 128 tokens</th>
|
||||
<th>Code Fixing<br>prefill: 1024 tokens<br>decode: 1024 tokens</th>
|
||||
<th>RAG<br>prefill: 1024 tokens<br>decode: 128 tokens</th>
|
||||
<th>Instruction Following<br>prefill: 256 tokens<br>decode: 128 tokens</th>
|
||||
<th>Multi-turn Chat<br>prefill: 512 tokens<br>decode: 256 tokens</th>
|
||||
<th>Large Summarization<br>prefill: 4096 tokens<br>decode: 512 tokens</th>
|
||||
</tr>
|
||||
<tr>
|
||||
<td style="vertical-align: middle;" rowspan="3" >A5000</td>
|
||||
<td>granite-3.1-2b-base</td>
|
||||
<td></td>
|
||||
<td>2.9</td>
|
||||
<td>10.2</td>
|
||||
<td>1.8</td>
|
||||
<td>8.2</td>
|
||||
<td>19.3</td>
|
||||
<td>9.1</td>
|
||||
<td>1.3</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>granite-3.1-2b-base-quantized.w8a8<br>(this model)</td>
|
||||
<td>1.13</td>
|
||||
<td>3.1</td>
|
||||
<td>12.1</td>
|
||||
<td>2.0</td>
|
||||
<td>9.6</td>
|
||||
<td>22.2</td>
|
||||
<td>10.2</td>
|
||||
<td>1.4</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>granite-3.1-2b-base-quantized.w4a16</td>
|
||||
<td>0.98</td>
|
||||
<td>2.8</td>
|
||||
<td>10.0</td>
|
||||
<td>1.8</td>
|
||||
<td>8.1</td>
|
||||
<td>18.6</td>
|
||||
<td>9.0</td>
|
||||
<td>1.2</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td style="vertical-align: middle;" rowspan="3" >A6000</td>
|
||||
<td>granite-3.1-2b-base</td>
|
||||
<td></td>
|
||||
<td>3.7</td>
|
||||
<td>12.4</td>
|
||||
<td>2.4</td>
|
||||
<td>10.3</td>
|
||||
<td>23.6</td>
|
||||
<td>11.0</td>
|
||||
<td>1.6</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>granite-3.1-2b-base-quantized.w8a8<br>(this model)</td>
|
||||
<td>1.12</td>
|
||||
<td>3.6</td>
|
||||
<td>14.4</td>
|
||||
<td>2.7</td>
|
||||
<td>12.0</td>
|
||||
<td>28.3</td>
|
||||
<td>12.9</td>
|
||||
<td>1.7</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>granite-3.1-2b-base-quantized.w4a16</td>
|
||||
<td>0.95</td>
|
||||
<td>3.7</td>
|
||||
<td>11.4</td>
|
||||
<td>2.5</td>
|
||||
<td>9.8</td>
|
||||
<td>22.1</td>
|
||||
<td>10.4</td>
|
||||
<td>1.4</td>
|
||||
</tr>
|
||||
</table>
|
||||
75
config.json
Normal file
75
config.json
Normal file
@@ -0,0 +1,75 @@
|
||||
{
|
||||
"_name_or_path": "ibm-granite/granite-3.1-2b-base",
|
||||
"architectures": [
|
||||
"GraniteForCausalLM"
|
||||
],
|
||||
"attention_bias": false,
|
||||
"attention_dropout": 0.1,
|
||||
"attention_multiplier": 0.015625,
|
||||
"bos_token_id": 0,
|
||||
"embedding_multiplier": 12.0,
|
||||
"eos_token_id": 0,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 2048,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 8192,
|
||||
"logits_scaling": 8.0,
|
||||
"max_position_embeddings": 131072,
|
||||
"mlp_bias": false,
|
||||
"model_type": "granite",
|
||||
"num_attention_heads": 32,
|
||||
"num_hidden_layers": 40,
|
||||
"num_key_value_heads": 8,
|
||||
"pad_token_id": 0,
|
||||
"quantization_config": {
|
||||
"config_groups": {
|
||||
"group_0": {
|
||||
"input_activations": {
|
||||
"actorder": null,
|
||||
"block_structure": null,
|
||||
"dynamic": true,
|
||||
"group_size": null,
|
||||
"num_bits": 8,
|
||||
"observer": null,
|
||||
"observer_kwargs": {},
|
||||
"strategy": "token",
|
||||
"symmetric": true,
|
||||
"type": "int"
|
||||
},
|
||||
"output_activations": null,
|
||||
"targets": [
|
||||
"Linear"
|
||||
],
|
||||
"weights": {
|
||||
"actorder": null,
|
||||
"block_structure": null,
|
||||
"dynamic": false,
|
||||
"group_size": null,
|
||||
"num_bits": 8,
|
||||
"observer": "mse",
|
||||
"observer_kwargs": {},
|
||||
"strategy": "channel",
|
||||
"symmetric": true,
|
||||
"type": "int"
|
||||
}
|
||||
}
|
||||
},
|
||||
"format": "int-quantized",
|
||||
"global_compression_ratio": 1.5301580068100984,
|
||||
"ignore": [
|
||||
"lm_head"
|
||||
],
|
||||
"kv_cache_scheme": null,
|
||||
"quant_method": "compressed-tensors",
|
||||
"quantization_status": "compressed"
|
||||
},
|
||||
"residual_multiplier": 0.22,
|
||||
"rms_norm_eps": 1e-05,
|
||||
"rope_scaling": null,
|
||||
"rope_theta": 5000000.0,
|
||||
"tie_word_embeddings": true,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.47.1",
|
||||
"use_cache": true,
|
||||
"vocab_size": 49152
|
||||
}
|
||||
1
configuration.json
Normal file
1
configuration.json
Normal file
@@ -0,0 +1 @@
|
||||
{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
|
||||
7
generation_config.json
Normal file
7
generation_config.json
Normal file
@@ -0,0 +1,7 @@
|
||||
{
|
||||
"_from_model_config": true,
|
||||
"bos_token_id": 0,
|
||||
"eos_token_id": 0,
|
||||
"pad_token_id": 0,
|
||||
"transformers_version": "4.47.1"
|
||||
}
|
||||
48892
merges.txt
Normal file
48892
merges.txt
Normal file
File diff suppressed because it is too large
Load Diff
3
model.safetensors
Normal file
3
model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:f56a1e0d6926e6042eef5f15b88daba17e5c547af4080db1e563561f0f2d4f6f
|
||||
size 2837639392
|
||||
21
recipe.yaml
Normal file
21
recipe.yaml
Normal file
@@ -0,0 +1,21 @@
|
||||
quant_stage:
|
||||
quant_modifiers:
|
||||
SmoothQuantModifier:
|
||||
smoothing_strength: 0.7
|
||||
mappings:
|
||||
- - ['re:.*q_proj', 're:.*k_proj', 're:.*v_proj']
|
||||
- re:.*input_layernorm
|
||||
- - ['re:.*gate_proj', 're:.*up_proj']
|
||||
- re:.*post_attention_layernorm
|
||||
- - ['re:.*down_proj']
|
||||
- re:.*up_proj
|
||||
GPTQModifier:
|
||||
sequential_update: true
|
||||
dampening_frac: 0.01
|
||||
ignore: [lm_head]
|
||||
config_groups:
|
||||
group_0:
|
||||
targets: [Linear]
|
||||
weights: {num_bits: 8, type: int, symmetric: true, strategy: channel, observer: mse}
|
||||
input_activations: {num_bits: 8, type: int, symmetric: true, strategy: token, dynamic: true,
|
||||
observer: memoryless}
|
||||
51
special_tokens_map.json
Normal file
51
special_tokens_map.json
Normal file
@@ -0,0 +1,51 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
"<|endoftext|>",
|
||||
"<fim_prefix>",
|
||||
"<fim_middle>",
|
||||
"<fim_suffix>",
|
||||
"<fim_pad>",
|
||||
"<filename>",
|
||||
"<gh_stars>",
|
||||
"<issue_start>",
|
||||
"<issue_comment>",
|
||||
"<issue_closed>",
|
||||
"<jupyter_start>",
|
||||
"<jupyter_text>",
|
||||
"<jupyter_code>",
|
||||
"<jupyter_output>",
|
||||
"<empty_output>",
|
||||
"<commit_before>",
|
||||
"<commit_msg>",
|
||||
"<commit_after>",
|
||||
"<reponame>"
|
||||
],
|
||||
"bos_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"unk_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
244936
tokenizer.json
Normal file
244936
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
188
tokenizer_config.json
Normal file
188
tokenizer_config.json
Normal file
@@ -0,0 +1,188 @@
|
||||
{
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
"0": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"1": {
|
||||
"content": "<fim_prefix>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"2": {
|
||||
"content": "<fim_middle>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"3": {
|
||||
"content": "<fim_suffix>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"4": {
|
||||
"content": "<fim_pad>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"5": {
|
||||
"content": "<filename>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"6": {
|
||||
"content": "<gh_stars>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"7": {
|
||||
"content": "<issue_start>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"8": {
|
||||
"content": "<issue_comment>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"9": {
|
||||
"content": "<issue_closed>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"10": {
|
||||
"content": "<jupyter_start>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"11": {
|
||||
"content": "<jupyter_text>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"12": {
|
||||
"content": "<jupyter_code>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"13": {
|
||||
"content": "<jupyter_output>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"14": {
|
||||
"content": "<empty_output>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"15": {
|
||||
"content": "<commit_before>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"16": {
|
||||
"content": "<commit_msg>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"17": {
|
||||
"content": "<commit_after>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"18": {
|
||||
"content": "<reponame>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|endoftext|>",
|
||||
"<fim_prefix>",
|
||||
"<fim_middle>",
|
||||
"<fim_suffix>",
|
||||
"<fim_pad>",
|
||||
"<filename>",
|
||||
"<gh_stars>",
|
||||
"<issue_start>",
|
||||
"<issue_comment>",
|
||||
"<issue_closed>",
|
||||
"<jupyter_start>",
|
||||
"<jupyter_text>",
|
||||
"<jupyter_code>",
|
||||
"<jupyter_output>",
|
||||
"<empty_output>",
|
||||
"<commit_before>",
|
||||
"<commit_msg>",
|
||||
"<commit_after>",
|
||||
"<reponame>"
|
||||
],
|
||||
"bos_token": "<|endoftext|>",
|
||||
"clean_up_tokenization_spaces": true,
|
||||
"eos_token": "<|endoftext|>",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 9223372036854775807,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"padding_side": "left",
|
||||
"tokenizer_class": "GPT2Tokenizer",
|
||||
"unk_token": "<|endoftext|>",
|
||||
"vocab_size": 49152
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user