Instructions to use spitfire4794/title-alpha-001 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use spitfire4794/title-alpha-001 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="spitfire4794/title-alpha-001", filename="Falcon-H1-Tiny-Multilingual-100M-Base.F16.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use spitfire4794/title-alpha-001 with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf spitfire4794/title-alpha-001:Q4_K_M # Run inference directly in the terminal: llama cli -hf spitfire4794/title-alpha-001:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf spitfire4794/title-alpha-001:Q4_K_M # Run inference directly in the terminal: llama cli -hf spitfire4794/title-alpha-001: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 spitfire4794/title-alpha-001:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf spitfire4794/title-alpha-001: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 spitfire4794/title-alpha-001:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf spitfire4794/title-alpha-001:Q4_K_M
Use Docker
docker model run hf.co/spitfire4794/title-alpha-001:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use spitfire4794/title-alpha-001 with Ollama:
ollama run hf.co/spitfire4794/title-alpha-001:Q4_K_M
- Unsloth Studio
How to use spitfire4794/title-alpha-001 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 spitfire4794/title-alpha-001 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 spitfire4794/title-alpha-001 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for spitfire4794/title-alpha-001 to start chatting
- Atomic Chat new
- Docker Model Runner
How to use spitfire4794/title-alpha-001 with Docker Model Runner:
docker model run hf.co/spitfire4794/title-alpha-001:Q4_K_M
- Lemonade
How to use spitfire4794/title-alpha-001 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull spitfire4794/title-alpha-001:Q4_K_M
Run and chat with the model
lemonade run user.title-alpha-001-Q4_K_M
List all available models
lemonade list
| { | |
| "architectures": [ | |
| "FalconH1ForCausalLM" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "attention_in_multiplier": 1.0, | |
| "attention_out_multiplier": 1.0, | |
| "bos_token_id": 1, | |
| "torch_dtype": "float16", | |
| "embedding_multiplier": 0.123046875, | |
| "eos_token_id": 11, | |
| "expansion_factor": 1.5, | |
| "head_dim": 64, | |
| "hidden_act": "silu", | |
| "hidden_size": 512, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 768, | |
| "key_multiplier": 1.0, | |
| "lm_head_multiplier": 0.078125, | |
| "mamba_chunk_size": 128, | |
| "mamba_conv_bias": true, | |
| "mamba_d_conv": 4, | |
| "mamba_d_head": 32, | |
| "mamba_d_ssm": 768, | |
| "mamba_d_state": 64, | |
| "mamba_expand": 2, | |
| "mamba_n_groups": 1, | |
| "mamba_n_heads": 24, | |
| "mamba_norm_before_gate": false, | |
| "mamba_proj_bias": false, | |
| "mamba_rms_norm": false, | |
| "mamba_use_mlp": true, | |
| "max_position_embeddings": 262144, | |
| "mlp_bias": false, | |
| "mlp_multipliers": [ | |
| 1.0, | |
| 1.0 | |
| ], | |
| "model_name": "tiiuae/Falcon-H1-Tiny-Multilingual-100M-Base", | |
| "model_type": "falcon_h1", | |
| "num_attention_heads": 8, | |
| "num_hidden_layers": 24, | |
| "num_key_value_heads": 2, | |
| "num_logits_to_keep": 1, | |
| "pad_token_id": 0, | |
| "projectors_bias": false, | |
| "rms_norm_eps": 1e-05, | |
| "rope_scaling": null, | |
| "rope_theta": 100000000000.0, | |
| "sliding_window": null, | |
| "ssm_in_multiplier": 1.0, | |
| "ssm_multipliers": [ | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0, | |
| 1.0 | |
| ], | |
| "ssm_out_multiplier": 1.0, | |
| "tie_word_embeddings": true, | |
| "time_step_floor": 0.0001, | |
| "time_step_max": 0.1, | |
| "time_step_min": 0.001, | |
| "time_step_rank": "auto", | |
| "unsloth_version": "2026.6.8", | |
| "use_cache": false, | |
| "vocab_size": 65536 | |
| } |