Create ReadMe

@article {zhang2024raft,
title={RAFT: Adapting Language Model to Domain Specific RAG},
author={Zhang, Tianjun and Patil, Shishir G and Jain, Naman and Shen, Sheng and Zaharia, Matei and Stoica, Ion and Gonzalez, Joseph E},
journal={arXiv preprint arXiv:2403.10131},
year={2024}
}

ReadMe

@@ -0,0 +1,91 @@
+---
+base_model: Qwen/Qwen2.5-32B-Instruct
+library_name: peft
+pipeline_tag: text-generation
+license: apache-2.0
+language:
+- en
+tags:
+- base_model:adapter:Qwen/Qwen2.5-32B-Instruct
+- lora
+- qlora
+- sft
+- raft
+- finance
+- rag
+- transformers
+- trl
+---
+
+# Model Card for UnifiedQ-Finance-RAFT
+
+**UnifiedQ-Finance-RAFT** is a specialized LoRA adapter for **Qwen 2.5 32B Instruct**, fine-tuned using the **RAFT (Retrieval-Augmented Fine-Tuning)** technique. It is designed to act as the reasoning engine for a quantitative finance RAG pipeline, specifically capable of distinguishing between relevant "oracle" documents and irrelevant "distractor" documents in complex options trading contexts.
+
+## Model Details
+
+### Model Description
+
+This model was trained to solve the "distractor problem" in RAG systems. Standard models often get confused when a retrieval system pulls in irrelevant documents alongside the correct ones. By using the RAFT methodology, this model was explicitly trained on a dataset where it had to ignore noise and reason only from the relevant text chunks to answer complex financial queries.
+
+- **Developed by:** Rednote (UnifiedQ Project)
+- **Model type:** LoRA Adapter (QLoRA 4-bit) for Causal LM
+- **Language(s):** English
+- **License:** Apache 2.0 (Inherited from Qwen 2.5)
+- **Finetuned from model:** [Qwen/Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct)
+
+### Model Sources
+
+- **Repository:** [More Information Needed - Link to your repo]
+- **Technique Paper:** [RAFT: Adapting Language Model to Domain Specific RAG](https://arxiv.org/abs/2403.10131)
+
+## Uses
+
+### Direct Use
+
+This model is intended to be used **with a RAG system** (Retrieval-Augmented Generation). It expects a prompt format that includes retrieved context documents (some relevant, some irrelevant) and a user question. It excels at:
+- Options trading strategies evaluation.
+- Risk management analysis.
+- Filtering noise from retrieved financial documents.
+
+### Out-of-Scope Use
+
+- General chat without context (it is specialized for document-based reasoning).
+- Financial advice (this is a research/development tool, not a financial advisor).
+
+## How to Get Started with the Model
+
+You can load this model using `peft` and `transformers`. Note that you must load the base model in 4-bit if running on consumer hardware.
+
+```python
+import torch
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+
+# 1. Load Base Model (Qwen 2.5 32B)
+base_model_id = "Qwen/Qwen2.5-32B-Instruct"
+adapter_model_id = "Rednote/Qwen-2.5-32B-RAFT-UnifiedQ" # Replace with your actual HF path
+
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16,
+    bnb_4bit_use_double_quant=True
+)
+
+base_model = AutoModelForCausalLM.from_pretrained(
+    base_model_id,
+    quantization_config=bnb_config,
+    device_map="auto",
+    trust_remote_code=True,
+    attn_implementation="flash_attention_2" # Optional: remove if no Flash Attn
+)
+
+# 2. Load the RAFT Adapter
+model = PeftModel.from_pretrained(base_model, adapter_model_id)
+tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True)
+
+# 3. Inference Example
+prompt = "Context: [Doc 1]... [Doc 2]... \n\n Question: How do I hedge delta risk?"
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_new_tokens=200)
+print(tokenizer.decode(outputs[0]))