Instructions to use Saravanankannan/Qwen-2.5-32B-RAFT-Finance-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Saravanankannan/Qwen-2.5-32B-RAFT-Finance-v1 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-32B-Instruct")
model = PeftModel.from_pretrained(base_model, "Saravanankannan/Qwen-2.5-32B-RAFT-Finance-v1")

Transformers

How to use Saravanankannan/Qwen-2.5-32B-RAFT-Finance-v1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Saravanankannan/Qwen-2.5-32B-RAFT-Finance-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Saravanankannan/Qwen-2.5-32B-RAFT-Finance-v1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Saravanankannan/Qwen-2.5-32B-RAFT-Finance-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Saravanankannan/Qwen-2.5-32B-RAFT-Finance-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Saravanankannan/Qwen-2.5-32B-RAFT-Finance-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Saravanankannan/Qwen-2.5-32B-RAFT-Finance-v1

SGLang

How to use Saravanankannan/Qwen-2.5-32B-RAFT-Finance-v1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Saravanankannan/Qwen-2.5-32B-RAFT-Finance-v1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Saravanankannan/Qwen-2.5-32B-RAFT-Finance-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Saravanankannan/Qwen-2.5-32B-RAFT-Finance-v1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Saravanankannan/Qwen-2.5-32B-RAFT-Finance-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Saravanankannan/Qwen-2.5-32B-RAFT-Finance-v1 with Docker Model Runner:
```
docker model run hf.co/Saravanankannan/Qwen-2.5-32B-RAFT-Finance-v1
```

Saravanankannan commited on Jan 3

Commit

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@@ -8,202 +8,92 @@ tags:
 - sft
 - transformers
 - trl
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
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-- **Developed by:** [More Information Needed]
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 ## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 ### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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 ### Out-of-Scope Use
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 ## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
 ### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 ## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-#### Speeds, Sizes, Times [optional]
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-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
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-#### Factors
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-### Results
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-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
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-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
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-### Framework versions
-- PEFT 0.18.0

 - sft
 - transformers
 - trl
+- finance
+- rag
+- raft
+license: mit
+language:
+- en
 ---
+# Model Card for UnifiedQ-Finance-RAFT
 ## Model Details
 ### Model Description
+**UnifiedQ-Finance-RAFT** is a specialized LoRA adapter for **Qwen 2.5 32B Instruct**, fine-tuned using the **RAFT (Retrieval-Augmented Fine-Tuning)** technique.
+This model was trained to act as the reasoning engine for a quantitative finance RAG pipeline. It addresses the "distractor problem" in RAG systems by being explicitly trained to distinguish between relevant "oracle" documents and irrelevant "distractor" documents when answering complex options trading and risk management queries.
+- **Developed by:** Rednote (UnifiedQ Project)
+- **Model type:** LoRA Adapter (QLoRA 4-bit) for Causal LM
+- **Language(s) (NLP):** English
+- **License:** MIT
+- **Finetuned from model:** [Qwen/Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct)
+### Model Sources
+- **Repository:** [Link to your Hugging Face Repo]
+- **Paper (Technique):** [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:
+- Evaluation of options trading strategies.
+- Quantitative 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).
+- Usage without 4-bit quantization on consumer hardware (due to the 32B parameter size).
 ## Bias, Risks, and Limitations
+The model is fine-tuned on specific financial domain data. It may hallucinate if provided with context documents that contain factually incorrect information (garbage in, garbage out). As a 32B model, it requires significant VRAM to run even with adapters.
 ### Recommendations
+Users should verify all financial outputs against standard models or verifiable sources. This model should be used as an assistant to a human trader, not an autonomous agent.
 ## How to Get Started with the Model
+Use the code below to get started with the model. Note that you must load the base model in 4-bit to fit on standard GPUs.
+```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
+)
+# Load base model
+base_model = AutoModelForCausalLM.from_pretrained(
+    base_model_id,
+    quantization_config=bnb_config,
+    device_map="auto",
+    trust_remote_code=True
+)
+# 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]))