Instructions to use OmsinghRotele/finance-tech-mistral-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OmsinghRotele/finance-tech-mistral-7b with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3")
model = PeftModel.from_pretrained(base_model, "OmsinghRotele/finance-tech-mistral-7b")

Transformers

How to use OmsinghRotele/finance-tech-mistral-7b with Transformers:

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

pipe = pipeline("text-generation", model="OmsinghRotele/finance-tech-mistral-7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("OmsinghRotele/finance-tech-mistral-7b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use OmsinghRotele/finance-tech-mistral-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OmsinghRotele/finance-tech-mistral-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OmsinghRotele/finance-tech-mistral-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OmsinghRotele/finance-tech-mistral-7b

SGLang

How to use OmsinghRotele/finance-tech-mistral-7b 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 "OmsinghRotele/finance-tech-mistral-7b" \
    --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": "OmsinghRotele/finance-tech-mistral-7b",
		"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 "OmsinghRotele/finance-tech-mistral-7b" \
        --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": "OmsinghRotele/finance-tech-mistral-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use OmsinghRotele/finance-tech-mistral-7b with Docker Model Runner:
```
docker model run hf.co/OmsinghRotele/finance-tech-mistral-7b
```

Model Card for finance-tech-mistral

A LoRA adapter fine-tuned on top of mistralai/Mistral-7B-Instruct-v0.3 for finance and technology domain tasks, trained using TRL.

⚠️ This is a LoRA adapter, not a standalone model. You must load the base Mistral-7B-Instruct-v0.3 model first and apply this adapter on top.

Quick Start

Install dependencies:

pip install transformers peft torch accelerate

Load and run:

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

base_model_id = "mistralai/Mistral-7B-Instruct-v0.3"

tokenizer = AutoTokenizer.from_pretrained(base_model_id)
model = AutoModelForCausalLM.from_pretrained(base_model_id, device_map="auto")

# Apply the LoRA adapter
model = PeftModel.from_pretrained(model, "OmsinghRotele/finance-tech-mistral-7b")

question = "What is a P/E ratio and how is it used in stock analysis?"
generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training Procedure

This model was trained with SFT (Supervised Fine-Tuning) on finance and technology domain data.

Framework Versions

PEFT 0.19.1
TRL: 1.5.0
Transformers: 5.0.0
Pytorch: 2.11.0+cu128
Datasets: 4.8.5
Tokenizers: 0.22.2

Citations

@software{vonwerra2020trl,
  title   = {{TRL: Transformers Reinforcement Learning}},
  author  = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
  license = {Apache-2.0},
  url     = {https://github.com/huggingface/trl},
  year    = {2020}
}

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