Instructions to use Akhenaton/sft_banking_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Akhenaton/sft_banking_model with Transformers:

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

pipe = pipeline("text-generation", model="Akhenaton/sft_banking_model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Akhenaton/sft_banking_model", dtype="auto")

llama-cpp-python

How to use Akhenaton/sft_banking_model with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Akhenaton/sft_banking_model",
	filename="unsloth.F16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Akhenaton/sft_banking_model with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Akhenaton/sft_banking_model:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Akhenaton/sft_banking_model:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Akhenaton/sft_banking_model:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Akhenaton/sft_banking_model:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Akhenaton/sft_banking_model:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Akhenaton/sft_banking_model:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Akhenaton/sft_banking_model:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Akhenaton/sft_banking_model:Q4_K_M

Use Docker

docker model run hf.co/Akhenaton/sft_banking_model:Q4_K_M

LM Studio
Jan

vLLM

How to use Akhenaton/sft_banking_model with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Akhenaton/sft_banking_model:Q4_K_M

SGLang

How to use Akhenaton/sft_banking_model 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 "Akhenaton/sft_banking_model" \
    --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": "Akhenaton/sft_banking_model",
		"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 "Akhenaton/sft_banking_model" \
        --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": "Akhenaton/sft_banking_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Akhenaton/sft_banking_model with Ollama:
```
ollama run hf.co/Akhenaton/sft_banking_model:Q4_K_M
```

Unsloth Studio new

How to use Akhenaton/sft_banking_model with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Akhenaton/sft_banking_model to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Akhenaton/sft_banking_model to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Akhenaton/sft_banking_model to start chatting

Pi new

How to use Akhenaton/sft_banking_model with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Akhenaton/sft_banking_model:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Akhenaton/sft_banking_model:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Akhenaton/sft_banking_model with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Akhenaton/sft_banking_model:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Akhenaton/sft_banking_model:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use Akhenaton/sft_banking_model with Docker Model Runner:
```
docker model run hf.co/Akhenaton/sft_banking_model:Q4_K_M
```

Lemonade

How to use Akhenaton/sft_banking_model with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Akhenaton/sft_banking_model:Q4_K_M

Run and chat with the model

lemonade run user.sft_banking_model-Q4_K_M

List all available models

lemonade list

Banking AI Assistant - Llama 3.2 1B Fine-tuned

A specialized banking and financial AI assistant fine-tuned on the T2-RAGBench dataset for conversational RAG tasks. This model excels at analyzing financial documents, answering banking-related questions, and providing detailed insights from financial reports.

Model Details

Developed by: Akhenaton
Model Type: Causal Language Model (Llama 3.2 1B)
License: Apache 2.0
Base Model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit
Fine-tuning Method: LoRA (Low-Rank Adaptation)
Training Framework: Unsloth + Hugging Face TRL
Quantization: 4-bit (BitsAndBytes)

Training Details

Dataset

Source: G4KMU/t2-ragbench (ConvFinQA subset)
Size: 32,908 context-independent QA pairs from 9,000+ financial documents
Domains: FinQA, ConvFinQA, VQAonBD, TAT-DQA
Focus: Financial documents with text and tables from SEC filings

Training Configuration

LoRA Parameters:
  r: 16
  lora_alpha: 16
  lora_dropout: 0
  target_modules: [q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj]

Training Setup:
  max_seq_length: 2048
  per_device_train_batch_size: 2
  gradient_accumulation_steps: 4
  max_steps: 60
  learning_rate: 2e-4
  optimizer: adamw_8bit
  lr_scheduler_type: cosine
  weight_decay: 0.01

Intended Use

Primary Use Cases

Financial Document Analysis: Extract insights from financial reports, SEC filings, and earnings statements
Banking Q&A: Answer questions about financial concepts, regulations, and banking operations
Conversational RAG: Provide context-aware responses based on financial document context
Financial Research: Assist with financial research and analysis tasks

Conversation Format

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

You are a specialized banking AI assistant. Analyze financial documents and provide accurate, detailed answers based on the given context. Focus on numerical accuracy and financial terminology.<|eot_id|><|start_header_id|>user<|end_header_id|>

Financial Document Context:
{context}

Question: {question}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

{response}<|eot_id|>

Usage

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained("Akhenaton/sft_banking_model")
tokenizer = AutoTokenizer.from_pretrained("Akhenaton/sft_banking_model")

# Prepare conversation
messages = [
    {"role": "user", "content": "Explain the key financial metrics in quarterly earnings."}
]

# Generate response
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
outputs = model.generate(inputs, max_new_tokens=128, temperature=1.5, min_p=0.1)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

With Unsloth (Recommended - 2x faster)

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    "Akhenaton/sft_banking_model",
    max_seq_length=2048,
    dtype=None,
    load_in_4bit=True
)
FastLanguageModel.for_inference(model)  # Enable fast inference

Available Formats

This model is available in multiple quantization formats:

q4_k_m: Recommended for most use cases
q8_0: Higher quality, more resource intensive
q5_k_m: Balanced quality and efficiency
f16: Full precision for maximum accuracy

Performance

Training Speed: 2x faster with Unsloth optimization
Memory Efficiency: 4-bit quantization reduces VRAM requirements
Inference Speed: Optimized for fast response generation
Accuracy: Specialized for financial domain with >80% context-independent Q&A capability

Limitations

Domain Specific: Optimized for financial/banking content, may have reduced performance on general topics
Training Size: Limited to 60 training steps - further training may improve performance
Context Length: Maximum sequence length of 2048 tokens
Language: English only
Numerical Reasoning: While improved for financial calculations, complex mathematical operations may require verification

Ethical Considerations

Financial Advice: This model should not be used as a substitute for professional financial advice
Data Source: Trained on public SEC filings and financial documents
Bias: May reflect biases present in financial reporting and documentation
Verification: Always verify numerical calculations and financial information from authoritative sources

Citation

If you use this model in your research or applications, please consider citing:

@misc{akhenaton2025sft_banking_model,
  author = {Akhenaton},
  title = {Banking AI Assistant - Llama 3.2 1B Fine-tuned},
  year = {2025},
  url = {https://huggingface.co/Akhenaton/sft_banking_model},
  note = {Fine-tuned with Unsloth on T2-RAGBench dataset}
}

Acknowledgments

Unsloth Team for the optimized training framework
Meta AI for the Llama 3.2 base model
G4KMU for the T2-RAGBench dataset
Hugging Face for the transformers library and model hosting

This model was trained 2x faster with Unsloth and Hugging Face's TRL library.

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Safetensors

Model size

1B params

Tensor type

BF16

F16