Text Generation
PEFT
Safetensors
Transformers
English
grpo
lora
sft
trl
unsloth
conversational
Eval Results (legacy)
Instructions to use marco-molinari/FinLM-Reasoning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use marco-molinari/FinLM-Reasoning with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-4-31b-it-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "marco-molinari/FinLM-Reasoning") - Transformers
How to use marco-molinari/FinLM-Reasoning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="marco-molinari/FinLM-Reasoning") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("marco-molinari/FinLM-Reasoning", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use marco-molinari/FinLM-Reasoning with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "marco-molinari/FinLM-Reasoning" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "marco-molinari/FinLM-Reasoning", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/marco-molinari/FinLM-Reasoning
- SGLang
How to use marco-molinari/FinLM-Reasoning 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 "marco-molinari/FinLM-Reasoning" \ --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": "marco-molinari/FinLM-Reasoning", "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 "marco-molinari/FinLM-Reasoning" \ --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": "marco-molinari/FinLM-Reasoning", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Studio new
How to use marco-molinari/FinLM-Reasoning 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 marco-molinari/FinLM-Reasoning 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 marco-molinari/FinLM-Reasoning to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for marco-molinari/FinLM-Reasoning to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="marco-molinari/FinLM-Reasoning", max_seq_length=2048, ) - Docker Model Runner
How to use marco-molinari/FinLM-Reasoning with Docker Model Runner:
docker model run hf.co/marco-molinari/FinLM-Reasoning
File size: 1,688 Bytes
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