Create README.md

README.md

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+---
+license: cc-by-nc-4.0
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-3B-Instruct
+pipeline_tag: text-generation
+tags:
+- finance
+---
+
+This is a toy model using CoT-sft with GRPO. 
+
+## Usage
+
+```
+tokenizer = AutoTokenizer.from_pretrained("yixuantt/Qwen2.5-3B-R1-Finance")
+
+model = AutoModelForCausalLM.from_pretrained("yixuantt/Qwen2.5-3B-R1-Finance",
+                                             torch_dtype = torch.bfloat16,
+                                             device_map = "auto"
+                                             )
+model.eval()
+
+print(model)
+def generate(text):
+    conv = [{"role": "system",
+             "content": "You are a helpful AI Assistant that provides well-reasoned and detailed responses. You first think about the reasoning process as an internal monologue and then provide the user with the answer."},{"role": "user", "content": text}]
+    prompt = tokenizer.apply_chat_template(conversation=conv, tokenize=False, add_generation_prompt=True)
+    encoded = tokenizer(prompt, return_tensors="pt")
+    generate_params = dict(
+        max_new_tokens=1024,
+        do_sample=True,
+        top_k=20,
+    )
+    with torch.no_grad():
+        generation_output = model.generate(input_ids=encoded.input_ids.cuda(),
+                                           attention_mask=encoded.attention_mask.cuda(),
+                                           tokenizer=tokenizer,
+                                           **generate_params)
+
+    generation_output = generation_output[:, encoded.input_ids.shape[1]:]
+    out = tokenizer.decode(generation_output[0], skip_special_tokens=True)
+    # print(out)
+    return out
+```