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

preview code

Raw

History Blame Contribute Delete

1.54 kB

	---
	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`](https://huggingface.co/eugenehp/sesame) \|
	\| Kind \| Staging redistrib of an upstream checkpoint for RLX runners. \|
	\| RLX crate \| [`rlx-sesame`](https://github.com/MIT-RLX/rlx-models/tree/main/crates/rlx-sesame) \|
	\| Upstream \| https://huggingface.co/unsloth/csm-1b \|

	## Quick start

	```bash
	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](https://github.com/MIT-RLX/rlx-models), place this repo under `weights/tts/sesame` (or pass the path explicitly), then:

	```bash
	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.