Instructions to use eugenehp/sesame with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use eugenehp/sesame 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 eugenehp/sesame # Run inference directly in the terminal: llama cli -hf eugenehp/sesame
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf eugenehp/sesame # Run inference directly in the terminal: llama cli -hf eugenehp/sesame
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 eugenehp/sesame # Run inference directly in the terminal: ./llama-cli -hf eugenehp/sesame
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 eugenehp/sesame # Run inference directly in the terminal: ./build/bin/llama-cli -hf eugenehp/sesame
Use Docker
docker model run hf.co/eugenehp/sesame
- LM Studio
- Jan
- Ollama
How to use eugenehp/sesame with Ollama:
ollama run hf.co/eugenehp/sesame
- Unsloth Studio
How to use eugenehp/sesame 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 eugenehp/sesame 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 eugenehp/sesame to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for eugenehp/sesame to start chatting
- Docker Model Runner
How to use eugenehp/sesame with Docker Model Runner:
docker model run hf.co/eugenehp/sesame
- Lemonade
How to use eugenehp/sesame with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull eugenehp/sesame
Run and chat with the model
lemonade run user.sesame-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
| { | |
| "architectures": [ | |
| "CsmForConditionalGeneration" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "audio_eos_token_id": 128003, | |
| "audio_token_id": 128002, | |
| "bos_token_id": 128000, | |
| "codebook_eos_token_id": 0, | |
| "codebook_pad_token_id": 2050, | |
| "codec_config": { | |
| "_name_or_path": "kyutai/mimi", | |
| "architectures": [ | |
| "MimiModel" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "audio_channels": 1, | |
| "codebook_dim": 256, | |
| "codebook_size": 2048, | |
| "compress": 2, | |
| "dilation_growth_rate": 2, | |
| "frame_rate": 12.5, | |
| "head_dim": 64, | |
| "hidden_act": "gelu", | |
| "hidden_size": 512, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 2048, | |
| "kernel_size": 7, | |
| "last_kernel_size": 3, | |
| "layer_scale_initial_scale": 0.01, | |
| "max_position_embeddings": 8000, | |
| "model_type": "mimi", | |
| "norm_eps": 1e-05, | |
| "normalize": false, | |
| "num_attention_heads": 8, | |
| "num_filters": 64, | |
| "num_hidden_layers": 8, | |
| "num_key_value_heads": 8, | |
| "num_quantizers": 32, | |
| "num_residual_layers": 1, | |
| "num_semantic_quantizers": 1, | |
| "pad_mode": "constant", | |
| "residual_kernel_size": 3, | |
| "rope_theta": 10000.0, | |
| "sampling_rate": 24000, | |
| "sliding_window": 250, | |
| "torch_dtype": "float32", | |
| "trim_right_ratio": 1.0, | |
| "upsample_groups": 512, | |
| "upsampling_ratios": [ | |
| 8, | |
| 6, | |
| 5, | |
| 4 | |
| ], | |
| "use_cache": false, | |
| "use_causal_conv": true, | |
| "use_conv_shortcut": false, | |
| "vector_quantization_hidden_dimension": 256 | |
| }, | |
| "depth_decoder_config": { | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "backbone_hidden_size": 2048, | |
| "head_dim": 128, | |
| "hidden_act": "silu", | |
| "hidden_size": 1024, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 8192, | |
| "max_position_embeddings": 33, | |
| "mlp_bias": false, | |
| "model_type": "csm_depth_decoder_model", | |
| "num_attention_heads": 8, | |
| "num_codebooks": 32, | |
| "num_hidden_layers": 4, | |
| "num_key_value_heads": 2, | |
| "rms_norm_eps": 1e-05, | |
| "rope_scaling": { | |
| "factor": 32.0, | |
| "high_freq_factor": 0.0078125, | |
| "low_freq_factor": 0.001953125, | |
| "original_max_position_embeddings": 16, | |
| "rope_type": "llama3" | |
| }, | |
| "rope_theta": 500000, | |
| "torch_dtype": "float32", | |
| "use_cache": true, | |
| "vocab_size": 2051 | |
| }, | |
| "head_dim": 64, | |
| "hidden_act": "silu", | |
| "hidden_size": 2048, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 8192, | |
| "max_position_embeddings": 2048, | |
| "mlp_bias": false, | |
| "model_type": "csm", | |
| "num_attention_heads": 32, | |
| "num_codebooks": 32, | |
| "num_hidden_layers": 16, | |
| "num_key_value_heads": 8, | |
| "pad_token_id": 128004, | |
| "rms_norm_eps": 1e-05, | |
| "rope_scaling": { | |
| "factor": 32.0, | |
| "high_freq_factor": 0.5, | |
| "low_freq_factor": 0.125, | |
| "original_max_position_embeddings": 1024, | |
| "rope_type": "llama3" | |
| }, | |
| "rope_theta": 500000, | |
| "text_vocab_size": 128256, | |
| "tie_codebooks_embeddings": true, | |
| "tie_word_embeddings": false, | |
| "torch_dtype": "float16", | |
| "transformers_version": "4.52.0.dev0", | |
| "use_cache": true, | |
| "vocab_size": 2051 | |
| } | |