Ritvik Gaur PRO

ritvik77

ritvik-gaur-76780b174

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Trying new things

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reacted to burtenshaw's post with 👍 1 day ago

Here’s a notebook to make Gemma reason with GRPO & TRL. I made this whilst prepping the next unit of the reasoning course: In this notebooks I combine together google’s model with some community tooling - First, I load the model from the Hugging Face hub with transformers’s latest release for Gemma 3 - I use PEFT and bitsandbytes to get it running on Colab - Then, I took Will Browns processing and reward functions to make reasoning chains from GSM8k - Finally, I used TRL’s GRPOTrainer to train the model Next step is to bring Unsloth AI in, then ship it in the reasoning course. Links to notebook below. https://colab.research.google.com/drive/1Vkl69ytCS3bvOtV9_stRETMthlQXR4wX?usp=sharing

replied to their post 2 days ago

Try it out: https://huggingface.co/ritvik77/Medical_Doctor_AI_LoRA-Mistral-7B-Instruct_FullModel 🩺 Medical Diagnosis AI Model - Powered by Mistral-7B & LoRA 🚀 🔹 Model Overview: Base Model: Mistral-7B (7.7 billion parameters) Fine-Tuning Method: LoRA (Low-Rank Adaptation) Quantization: bnb_4bit (reduces memory footprint while retaining performance) 🔹 Parameter Details: Original Mistral-7B Parameters: 7.7 billion LoRA Fine-Tuned Parameters: 4.48% of total model parameters (340 million) Final Merged Model Size (bnb_4bit Quantized): ~4.5GB This can help you in making a AI agent for healthcare, if you need to finetune it for JSON function/tool calling format you can use some medical function calling dataset to again fine fine tine on it.

posted an update 3 days ago

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Try it out: ritvik77/Medical_Doctor_AI_LoRA-Mistral-7B-Instruct_FullModel

🩺 Medical Diagnosis AI Model - Powered by Mistral-7B & LoRA 🚀
🔹 Model Overview:
Base Model: Mistral-7B (7.7 billion parameters)
Fine-Tuning Method: LoRA (Low-Rank Adaptation)
Quantization: bnb_4bit (reduces memory footprint while retaining performance)
🔹 Parameter Details:
Original Mistral-7B Parameters: 7.7 billion
LoRA Fine-Tuned Parameters: 4.48% of total model parameters (340 million) Final Merged Model Size (bnb_4bit Quantized): ~4.5GB

This can help you in making a AI agent for healthcare, if you need to finetune it for JSON function/tool calling format you can use some medical function calling dataset to again fine fine tine on it.

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Hey Community,

ritvik77/FineTune_LoRA__AgentToolCall_Mistral-7B_Transformer

Fine-tuned Mistral-7B-Instruct-v0.3 with LoRA on Salesforce Function Dataset (~60K samples) for AI finance Agent, deployed full scale LLM model (14.5GB) via Hugging Face python transformers library and LoRA (PEFT, 715MB) for scalability.

Please let me know any improvements it requires, your ideas and feedback are always welcomed as I am still new to this and still learning.

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