Instructions to use EREN121232/MAJESTIC-FIN-R1-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use EREN121232/MAJESTIC-FIN-R1-gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="EREN121232/MAJESTIC-FIN-R1-gguf", filename="MAJESTIC-FIN-R1-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 EREN121232/MAJESTIC-FIN-R1-gguf with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf EREN121232/MAJESTIC-FIN-R1-gguf:F16 # Run inference directly in the terminal: llama-cli -hf EREN121232/MAJESTIC-FIN-R1-gguf:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf EREN121232/MAJESTIC-FIN-R1-gguf:F16 # Run inference directly in the terminal: llama-cli -hf EREN121232/MAJESTIC-FIN-R1-gguf:F16
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 EREN121232/MAJESTIC-FIN-R1-gguf:F16 # Run inference directly in the terminal: ./llama-cli -hf EREN121232/MAJESTIC-FIN-R1-gguf:F16
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 EREN121232/MAJESTIC-FIN-R1-gguf:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf EREN121232/MAJESTIC-FIN-R1-gguf:F16
Use Docker
docker model run hf.co/EREN121232/MAJESTIC-FIN-R1-gguf:F16
- LM Studio
- Jan
- vLLM
How to use EREN121232/MAJESTIC-FIN-R1-gguf with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "EREN121232/MAJESTIC-FIN-R1-gguf" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "EREN121232/MAJESTIC-FIN-R1-gguf", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/EREN121232/MAJESTIC-FIN-R1-gguf:F16
- Ollama
How to use EREN121232/MAJESTIC-FIN-R1-gguf with Ollama:
ollama run hf.co/EREN121232/MAJESTIC-FIN-R1-gguf:F16
- Unsloth Studio new
How to use EREN121232/MAJESTIC-FIN-R1-gguf 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 EREN121232/MAJESTIC-FIN-R1-gguf 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 EREN121232/MAJESTIC-FIN-R1-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for EREN121232/MAJESTIC-FIN-R1-gguf to start chatting
- Pi new
How to use EREN121232/MAJESTIC-FIN-R1-gguf with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf EREN121232/MAJESTIC-FIN-R1-gguf:F16
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": "EREN121232/MAJESTIC-FIN-R1-gguf:F16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use EREN121232/MAJESTIC-FIN-R1-gguf with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf EREN121232/MAJESTIC-FIN-R1-gguf:F16
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 EREN121232/MAJESTIC-FIN-R1-gguf:F16
Run Hermes
hermes
- Docker Model Runner
How to use EREN121232/MAJESTIC-FIN-R1-gguf with Docker Model Runner:
docker model run hf.co/EREN121232/MAJESTIC-FIN-R1-gguf:F16
- Lemonade
How to use EREN121232/MAJESTIC-FIN-R1-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull EREN121232/MAJESTIC-FIN-R1-gguf:F16
Run and chat with the model
lemonade run user.MAJESTIC-FIN-R1-gguf-F16
List all available models
lemonade list
| { | |
| "architectures": [ | |
| "Lfm2ForCausalLM" | |
| ], | |
| "block_auto_adjust_ff_dim": false, | |
| "block_dim": 2048, | |
| "block_ffn_dim_multiplier": 1.0, | |
| "block_mlp_init_scale": 1.0, | |
| "block_multiple_of": 256, | |
| "block_norm_eps": 1e-05, | |
| "block_out_init_scale": 1.0, | |
| "block_use_swiglu": true, | |
| "block_use_xavier_init": true, | |
| "bos_token_id": 1, | |
| "conv_L_cache": 3, | |
| "conv_bias": false, | |
| "conv_dim": 2048, | |
| "conv_dim_out": 2048, | |
| "conv_use_xavier_init": true, | |
| "torch_dtype": "float16", | |
| "eos_token_id": 7, | |
| "full_attn_idxs": null, | |
| "hidden_size": 2048, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 10752, | |
| "layer_types": [ | |
| "conv", | |
| "conv", | |
| "full_attention", | |
| "conv", | |
| "conv", | |
| "full_attention", | |
| "conv", | |
| "conv", | |
| "conv", | |
| "full_attention", | |
| "conv", | |
| "conv", | |
| "conv", | |
| "full_attention", | |
| "conv", | |
| "conv", | |
| "conv", | |
| "full_attention", | |
| "conv", | |
| "conv", | |
| "conv", | |
| "full_attention", | |
| "conv", | |
| "conv", | |
| "full_attention", | |
| "conv", | |
| "conv", | |
| "full_attention", | |
| "conv", | |
| "conv" | |
| ], | |
| "max_position_embeddings": 128000, | |
| "model_name": "LiquidAI/LFM2-2.6B", | |
| "model_type": "lfm2", | |
| "norm_eps": 1e-05, | |
| "num_attention_heads": 32, | |
| "num_heads": 32, | |
| "num_hidden_layers": 30, | |
| "num_key_value_heads": 8, | |
| "pad_token_id": 0, | |
| "rope_parameters": { | |
| "rope_theta": 1000000.0, | |
| "rope_type": "default" | |
| }, | |
| "theta": 1000000.0, | |
| "tie_word_embeddings": true, | |
| "unsloth_version": "2026.4.8", | |
| "use_cache": false, | |
| "use_pos_enc": true, | |
| "vocab_size": 65536 | |
| } |