46 lines
1.6 KiB
Markdown
46 lines
1.6 KiB
Markdown
---
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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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``` |