---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
---

```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/Qwen2.5-Coder-7B-Instruct"
model_out = "Qwen2.5-Coder-7B-Instruct.w8a8"

num_samples = 128
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,
    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=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,
)

model.save_pretrained(model_out)
```