初始化项目，由ModelHub XC社区提供模型

Model: nytopop/Qwen3-8B.w8a8
Source: Original Platform

README.md

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+---
+library_name: transformers
+license: apache-2.0
+license_link: https://huggingface.co/Qwen/Qwen3-8B/blob/main/LICENSE
+pipeline_tag: text-generation
+base_model: Qwen/Qwen3-8B
+---
+
+Int8 quant for optimized performance on Ampere.
+
+# usage
+
+```shell
+uv venv --python 3.12
+
+uv pip install sglang[all] --find-links https://flashinfer.ai/whl/cu124/torch2.5/flashinfer-python
+
+uv run python -m sglang.launch_server --model-path nytopop/Qwen3-8B.w8a8 --quantization w8a8_int8 --reasoning-parser qwen3
+```
+
+# creation
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from datasets import load_dataset
+from llmcompressor import oneshot
+from llmcompressor.modifiers.quantization import GPTQModifier
+from llmcompressor.modifiers.smoothquant import SmoothQuantModifier
+
+model_id  = "Qwen/Qwen3-8B"
+model_out = "Qwen3-8B.w8a8"
+
+num_samples = 256
+max_seq_len = 4096
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+def preprocess_fn(example):
+  return {"text": tokenizer.apply_chat_template(example["messages"], add_generation_prompt=False, tokenize=False)}
+
+ds = load_dataset("neuralmagic/LLM_compression_calibration", split="train")
+ds = ds.shuffle().select(range(num_samples))
+ds = ds.map(preprocess_fn)
+
+recipe = [
+  SmoothQuantModifier(smoothing_strength=0.7),
+  GPTQModifier(sequential=True,targets="Linear",scheme="W8A8",ignore=["lm_head"],dampening_frac=0.01),
+]
+
+model = AutoModelForCausalLM.from_pretrained(
+  model_id,
+  device_map="auto",
+  torch_dtype="bfloat16",
+)
+
+oneshot(
+  model=model,
+  dataset=ds,
+  recipe=recipe,
+  max_seq_length=max_seq_len,
+  num_calibration_samples=num_samples,
+  output_dir=model_out,
+)
+```