How to use from
llama.cppInstall 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/sesameUse 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/sesameBuild 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/sesameUse Docker
docker model run hf.co/eugenehp/sesameQuick Links
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.
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Install (macOS, Linux)
# 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