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

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

sesame / README.md

eugenehp

Upload README.md with huggingface_hub

299c7a8 verified 21 days ago

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metadata

license: apache-2.0
pipeline_tag: text-to-speech
library_name: rlx
tags:
  - sesame
  - csm
  - tts
  - rlx

Sesame CSM-1B (RLX staging)

CSM-1B conversational TTS weights (ungated mirror) for RLX.

Field	Value
Hub id	`eugenehp/sesame`
Kind	Staging redistrib of an upstream checkpoint for RLX runners.
RLX crate	`rlx-sesame`
Upstream	https://huggingface.co/unsloth/csm-1b

Quick start

hf download eugenehp/sesame --local-dir .
cargo run -p rlx-sesame --release -- --model-dir .

File highlights

model.safetensors (3.9 GiB)
sesame-csm-backbone.gguf (1.2 GiB)
tokenizer.json (16.4 MiB)
tokenizer_config.json (49.4 KiB)
config.json (3.0 KiB)
chat_template.jinja (2.0 KiB)
special_tokens_map.json (449 B)
preprocessor_config.json (271 B)
generation_config.json (264 B)

Run with RLX

Clone rlx-models, place this repo under weights/tts/sesame (or pass the path explicitly), then:

cargo run -p rlx-sesame --release -- --model-dir .

License

Apache License 2.0 — see LICENSE. Inherit upstream terms when redistributing.

Original weights and authorship: https://huggingface.co/unsloth/csm-1b

Redistrib note

This Hub repo exists so RLX recipes have a stable fetch target. When you only need the upstream checkpoint, prefer the Upstream link above.