65 lines
1.6 KiB
Markdown
65 lines
1.6 KiB
Markdown
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---
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library_name: transformers
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license: apache-2.0
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license_link: https://huggingface.co/Qwen/Qwen3-1.7B/blob/main/LICENSE
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pipeline_tag: text-generation
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base_model: Qwen/Qwen3-1.7B
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---
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Int8 quant for optimized performance on Ampere.
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# usage
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```shell
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uv venv --python 3.12
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uv pip install sglang[all] --find-links https://flashinfer.ai/whl/cu124/torch2.5/flashinfer-python
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uv run python -m sglang.launch_server --model-path nytopop/Qwen3-1.7B.w8a8 --quantization w8a8_int8 --reasoning-parser qwen3
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```
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# creation
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from datasets import load_dataset
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from llmcompressor import oneshot
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from llmcompressor.modifiers.quantization import GPTQModifier
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from llmcompressor.modifiers.smoothquant import SmoothQuantModifier
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model_id = "Qwen/Qwen3-1.7B"
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model_out = "Qwen3-1.7B.w8a8"
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num_samples = 256
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max_seq_len = 4096
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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def preprocess_fn(example):
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return {"text": tokenizer.apply_chat_template(example["messages"], add_generation_prompt=False, tokenize=False)}
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ds = load_dataset("neuralmagic/LLM_compression_calibration", split="train")
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ds = ds.shuffle().select(range(num_samples))
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ds = ds.map(preprocess_fn)
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recipe = [
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SmoothQuantModifier(smoothing_strength=0.7),
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GPTQModifier(sequential=True,targets="Linear",scheme="W8A8",ignore=["lm_head"],dampening_frac=0.01),
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]
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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device_map="auto",
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torch_dtype="bfloat16",
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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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max_seq_length=max_seq_len,
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num_calibration_samples=num_samples,
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output_dir=model_out,
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)
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```
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