89 lines
2.5 KiB
Markdown
89 lines
2.5 KiB
Markdown
---
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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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library_name: transformers
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---
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# AbleCredit Reasoner R0 Qwen 2.5 3B Instruct
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## Introduction
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This model is trained by Deepseek R1 style (GRPO) reinforcement learning on Qwen 2.5 3B Instruct as a base model.
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Primarily intended for research in application of small LLMs trained using GRPO/RL in the domain of finance, credit underwriting etc.
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### Model Description
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- **Fine Tuned by:** AbleCredit (LightBees Technologies Private Limited, Bengaluru, India)
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- **License:** We've retained the original Qwen research license. Note that license does not allow commercial use.
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- **Finetuned from model:** Qwen/Qwen2.5-3B-Instruct
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## How to Get Started with the Model
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Use with standard Huggingface based setup
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```python
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model_name = "AbleCredit/AbleCredit-R0-Qwen-2.5-3B-Instruct" # or local path to model
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system_prompt = {
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"role": "system",
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"content": (
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"You are a helpful assistant. User asks a question the assistant answers it.\n"
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"The assistant first thinks about reasoning process in mind and then provides the user with the answer."
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),
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}
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suffix_prompt = {
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"role": "assistant",
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"content": "Let me solve this step by step.\n<think>",
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}
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prompt_msgs = [
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system_prompt,
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{"role": "user", "content": "What is 15 times 3 ?"},
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suffix_prompt,
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]
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base_model = AutoModelForCausalLM.from_pretrained(
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model_name,
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device_map="auto",
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torch_dtype=torch.bfloat16,
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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prompt = tokenizer.apply_chat_template(
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prompt_msgs,
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tokenize=False,
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continue_final_message=True,
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add_generation_prompt=False,
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)
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# Tokenize the prompt and move it to the appropriate device.
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda:0")
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print("\nGenerating response...\n")
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outputs = model.generate(
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**inputs,
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max_new_tokens=1024,
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temperature=0.5,
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min_p=0.01,
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)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print("\nResponse:\n", response)
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```
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## Training Details
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### Training Data
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Trained using open source logical reasoning datasets and a proprietary finance dataset created by AbleCredit.com.
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### Training Procedure
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Trained using deepseek style reinforcement learning using GRPO with rule based rewards.
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## Evaluation
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- Model achieves ~67% score on GSM8K benchmark in a **zero shot** setting (check benchmarking script for more details).
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## Model Card Contact
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[contact Harshad Saykhedkar via LinkedIn](https://www.linkedin.com/in/harshadss/) |