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Model: AbleCredit/AbleCredit-R0-Qwen-2.5-3B-Instruct
Source: Original Platform

README.md

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