kingabzpro
/

Phi-4-Reasoning-Plus-FinQA-COT

@@ -1,199 +1,138 @@
 ---
 library_name: transformers
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 ---
 library_name: transformers
+tags:
+- finance
+- xnet
+- phi
+license: apache-2.0
+datasets:
+- TheFinAI/Fino1_Reasoning_Path_FinQA
+language:
+- en
+base_model:
+- microsoft/Phi-4-reasoning-plus
+pipeline_tag: text-generation
 ---
+# Fine-tuning Phi-4-reasoning-plus on FinQA Dataset
+This project fine-tunes the [`microsoft/Phi-4-reasoning-plus`](https://huggingface.co/microsoft/Phi-4-reasoning-plus) model using a medical reasoning dataset (`TheFinAI/Fino1_Reasoning_Path_FinQA`).
+---
+## Setup
+1. Install the required libraries:
+```bash
+pip install -U datasets accelerate peft trl bitsandbytes
+pip install -U transformers
+pip install huggingface_hub[hf_xet]
+```
+2. Authenticate with Hugging Face Hub:
+Make sure your Hugging Face token is stored in an environment variable:
+```bash
+export HF_TOKEN=your_huggingface_token
+```
+The notebook will automatically log you in using this token.
+---
+## How to Run
+1. **Load the Model and Tokenizer**
+   The script downloads the full Phi-4-reasoning-plus model.
+2. **Prepare the Dataset**
+   - The notebook uses `TheFinAI/Fino1_Reasoning_Path_FinQA` (first 1000 samples).
+   - It formats each example into an **instruction-following prompt** with step-by-step chain-of-thought reasoning.
+3. **Fine-tuning**
+   - Fine-tuning is set up with PEFT (LoRA / Adapter Tuning style) to modify a small subset of model parameters.
+   - TRL (Transformer Reinforcement Learning) is used to fine-tune efficiently.
+4. **Push Fine-tuned Model**
+   - After training, the fine-tuned model and tokenizer are pushed back to your Hugging Face account.
+---
+>> **Here is the training notebook:** [Fine_tuning_Phi-4-Reasoning-Plus](https://huggingface.co/kingabzpro/Phi-4-Reasoning-Plus-FinQA-COT/blob/main/fine-tuning-phi-4-reasoning.ipynb)
+## Model Configuration
+- **Base Model**: `microsoft/Phi-4-reasoning-plus`
+- **Training**: PEFT + TRL
+- **Dataset**: 1000 examples FinQA reasoning dataset
+---
+## Notes
+- **GPU Required**: Make sure you have access to 1X A100s. Get it from RunPod for an hours. Training took only 7 minutes.
+- **Environment**: The notebook expects an environment where NVIDIA CUDA drivers are available (`nvidia-smi` check is included).
+---
+## Example Prompt Format
+```
+<|im_start|>system<|im_sep|>
+Below is an instruction that describes a task, paired with an input that provides further context.
+Write a response that appropriately completes the request.
+Before answering, think carefully about the question and create a step-by-step chain of thoughts to ensure a logical and accurate response.
+<|im_end|>
+<|im_start|>user<|im_sep|>
+{}<|im_end|>
+<|im_start|>assistant<|im_sep|>
+<think>
+{}
+</think>
+{}
+```
+---
+## Usage Script (not-tested)
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+import torch
+# Base model (original model from Meta)
+base_model_id = "microsoft/Phi-4-reasoning-plus"
+# Your fine-tuned LoRA adapter repository
+lora_adapter_id = "kingabzpro/Phi-4-Reasoning-Plus-FinQA-COT"
+# Load base model
+base_model = AutoModelForCausalLM.from_pretrained(
+    base_model_id,
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
+    trust_remote_code=True,
+)
+# Attach the LoRA adapter
+model = PeftModel.from_pretrained(
+    base_model,
+    lora_adapter_id,
+    device_map="auto",
+    trust_remote_code=True,
+)
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True)
+# Inference example
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=1200)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```