Create README.md

README.md

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+---
+base_model:
+- Qwen/Qwen2.5-32B-Instruct
+---
+
+## Model Overview
+
+This repository, `ModelFuture-Distill-Qwen-32B-SFT-v1`, is designed for testing purposes. We directly apply Supervised Fine-Tuning (SFT) to the base model.
+
+## Intended Use
+
+This model is primarily intended for testing and validation purposes. It can be used to:
+- Evaluate the performance of the distilled model on various tasks.
+- Test the functionality and robustness of the model in different environments.
+- Provide a baseline for further development and optimization.
+
+Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "zhuguoku/ModelFuture-Distill-Qwen-32B-SFT-v1"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "我想锻炼身体，给我提供一些建议。"
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+```