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.
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]
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