Instructions to use shah-shazid-askary/amd-finance-llm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shah-shazid-askary/amd-finance-llm with Transformers:

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

pipe = pipeline("text-generation", model="shah-shazid-askary/amd-finance-llm")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("shah-shazid-askary/amd-finance-llm", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use shah-shazid-askary/amd-finance-llm with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shah-shazid-askary/amd-finance-llm"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shah-shazid-askary/amd-finance-llm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/shah-shazid-askary/amd-finance-llm

SGLang

How to use shah-shazid-askary/amd-finance-llm 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 "shah-shazid-askary/amd-finance-llm" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shah-shazid-askary/amd-finance-llm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "shah-shazid-askary/amd-finance-llm" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shah-shazid-askary/amd-finance-llm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use shah-shazid-askary/amd-finance-llm with Docker Model Runner:
```
docker model run hf.co/shah-shazid-askary/amd-finance-llm
```

AMD Finance LLM

Domain-specific large language model fine-tuned for finance, optimized for AMD Instinct MI300X GPUs via ROCm.

Model Details

Base Model: Qwen/Qwen2.5-7B-Instruct
Parameters: 7.6B
Architecture: Qwen2
Fine-tuned on: 500K financial instruction examples

Training Recipe

Derived from ODA-Fin (arXiv:2603.07223) + Fin-R1 (arXiv:2503.16252):

Hyperparameter	Value
Learning rate	1e-5 (cosine, 10% warmup)
Epochs	3
Batch size	1 × 16 grad accum
Sequence length	8192
Precision	bfloat16
Attention	Flash Attention 2 (CK backend)

Hardware Stack

AMD Instinct MI300X (192GB HBM3)
ROCm 6.2 + PyTorch
Hugging Face Optimum-AMD + vLLM for serving

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("shah-shazid-askary/amd-finance-llm")
tokenizer = AutoTokenizer.from_pretrained("shah-shazid-askary/amd-finance-llm")

messages = [{"role": "user", "content": "What is a P/E ratio?"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Demo Space: shah-shazid-askary/amd-finance-llm-project
Training Script: train.py

Generated by ML Intern

This model repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.

Try ML Intern: https://smolagents-ml-intern.hf.space
Source code: https://github.com/huggingface/ml-intern

Downloads last month: -; Downloads are not tracked for this model. How to track

Model tree for shah-shazid-askary/amd-finance-llm

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Finetuned

(2570)

this model

Dataset used to train shah-shazid-askary/amd-finance-llm

Spaces using shah-shazid-askary/amd-finance-llm 2

Papers for shah-shazid-askary/amd-finance-llm

Unlocking Data Value in Finance: A Study on Distillation and Difficulty-Aware Training

Paper • 2603.07223 • Published Mar 7 • 13

Fin-R1: A Large Language Model for Financial Reasoning through Reinforcement Learning

Paper • 2503.16252 • Published Mar 20, 2025 • 32

shah-shazid-askary
/

amd-finance-llm