Instructions to use Akshint47/Nano_R1_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use Akshint47/Nano_R1_Model with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("<base-model-id>") model.load_adapter("Akshint47/Nano_R1_Model", set_active=True) - Transformers
How to use Akshint47/Nano_R1_Model with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Akshint47/Nano_R1_Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
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# Fine-Tuning Qwen2.5-3B-Instruct with GRPO for GSM8K Dataset
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This notebook demonstrates the process of fine-tuning the **Qwen2.5-3B-Instruct** model using **GRPO (Generalized Reward Policy Optimization)** on the **GSM8K** dataset. The goal is to improve the model's ability to solve mathematical reasoning problems by leveraging reinforcement learning with custom reward functions.
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---
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license: apache-2.0
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language:
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- en
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base_model:
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- unsloth/Qwen2.5-3B-Instruct-unsloth-bnb-4bit
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library_name: adapter-transformers
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- qwen2
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- trl
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- grpo
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---
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# Uploaded model
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- **Developed by:** Akshint47
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- **License:** apache-2.0
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- **Finetuned from model :** unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit
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This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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# Fine-Tuning Qwen2.5-3B-Instruct with GRPO for GSM8K Dataset
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This notebook demonstrates the process of fine-tuning the **Qwen2.5-3B-Instruct** model using **GRPO (Generalized Reward Policy Optimization)** on the **GSM8K** dataset. The goal is to improve the model's ability to solve mathematical reasoning problems by leveraging reinforcement learning with custom reward functions.
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