Update README.md

README.md

@@ -7,6 +7,11 @@ tags:
 - unsloth
 - sft
 licence: license
+datasets:
+- RaagulQB/alpaca_coding_dataset_full
+- tatsu-lab/alpaca
+base_model:
+- Qwen/Qwen3-0.6B-Base
 ---
 
 # Model Card for Qwen3-0.6B-Alpaca
@@ -17,12 +22,34 @@ It has been trained using [TRL](https://github.com/huggingface/trl).
 ## Quick start
 
 ```python
-from transformers import pipeline
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
-generator = pipeline("text-generation", model="None", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
+model_name='wesjos/Qwen3-0.6B-Alpaca'
+
+model=AutoModelForCausalLM.from_pretrained(model_name)
+tokenizer=AutoTokenizer.from_pretrained(model_name)
+
+alpaca_prompt = """"Below is an instruction that describes a task. Write a response that appropriately completes the request.
+
+### Instruction:
+{}
+
+### Input:
+{}
+
+### Response:
+"""
+
+inputs = tokenizer(
+[
+    alpaca_prompt.format(
+        "完成以下代码要求", # instruction
+        "使用python写一个transformer神经网络" #Input
+    )
+], return_tensors = "pt").to("cuda")
+
+outputs = model.generate(**inputs, max_new_tokens=1024, use_cache=True,temperature=0.6,do_sample=True,top_p=0.95,top_k=20)
+print(tokenizer.batch_decode(outputs)[0])
 ```
 
 ## Training procedure