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--- |
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license: cc-by-nc-4.0 |
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language: |
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- en |
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base_model: |
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- Qwen/Qwen2.5-3B-Instruct |
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pipeline_tag: text-generation |
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tags: |
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- finance |
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--- |
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This is a toy model using CoT-sft with GRPO. |
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## Usage |
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``` |
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tokenizer = AutoTokenizer.from_pretrained("yixuantt/Qwen2.5-3B-R1-Finance") |
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model = AutoModelForCausalLM.from_pretrained("yixuantt/Qwen2.5-3B-R1-Finance", |
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torch_dtype = torch.bfloat16, |
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device_map = "auto" |
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) |
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model.eval() |
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print(model) |
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def generate(text): |
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conv = [{"role": "system", |
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"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}] |
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prompt = tokenizer.apply_chat_template(conversation=conv, tokenize=False, add_generation_prompt=True) |
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encoded = tokenizer(prompt, return_tensors="pt") |
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generate_params = dict( |
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max_new_tokens=1024, |
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do_sample=True, |
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top_k=20, |
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) |
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with torch.no_grad(): |
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generation_output = model.generate(input_ids=encoded.input_ids.cuda(), |
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attention_mask=encoded.attention_mask.cuda(), |
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tokenizer=tokenizer, |
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**generate_params) |
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generation_output = generation_output[:, encoded.input_ids.shape[1]:] |
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out = tokenizer.decode(generation_output[0], skip_special_tokens=True) |
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# print(out) |
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return out |
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``` |
