Instructions to use ZelligeAI/tessera-compressor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ZelligeAI/tessera-compressor with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ZelligeAI/tessera-compressor",
	filename="gguf/compressor-v31-q8_0.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ZelligeAI/tessera-compressor 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 ZelligeAI/tessera-compressor:Q8_0
# Run inference directly in the terminal:
llama cli -hf ZelligeAI/tessera-compressor:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ZelligeAI/tessera-compressor:Q8_0
# Run inference directly in the terminal:
llama cli -hf ZelligeAI/tessera-compressor:Q8_0

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 ZelligeAI/tessera-compressor:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf ZelligeAI/tessera-compressor:Q8_0

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 ZelligeAI/tessera-compressor:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ZelligeAI/tessera-compressor:Q8_0

Use Docker

docker model run hf.co/ZelligeAI/tessera-compressor:Q8_0

LM Studio
Jan

vLLM

How to use ZelligeAI/tessera-compressor with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ZelligeAI/tessera-compressor"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ZelligeAI/tessera-compressor",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ZelligeAI/tessera-compressor:Q8_0

Ollama
How to use ZelligeAI/tessera-compressor with Ollama:
```
ollama run hf.co/ZelligeAI/tessera-compressor:Q8_0
```

Unsloth Studio

How to use ZelligeAI/tessera-compressor 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 ZelligeAI/tessera-compressor 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 ZelligeAI/tessera-compressor to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ZelligeAI/tessera-compressor to start chatting

How to use ZelligeAI/tessera-compressor with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ZelligeAI/tessera-compressor:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "ZelligeAI/tessera-compressor:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ZelligeAI/tessera-compressor with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ZelligeAI/tessera-compressor:Q8_0

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default ZelligeAI/tessera-compressor:Q8_0

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use ZelligeAI/tessera-compressor with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ZelligeAI/tessera-compressor:Q8_0

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "ZelligeAI/tessera-compressor:Q8_0" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use ZelligeAI/tessera-compressor with Docker Model Runner:
```
docker model run hf.co/ZelligeAI/tessera-compressor:Q8_0
```

Lemonade

How to use ZelligeAI/tessera-compressor with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ZelligeAI/tessera-compressor:Q8_0

Run and chat with the model

lemonade run user.tessera-compressor-Q8_0

List all available models

lemonade list

blobbybob commited on 2 days ago

Commit

befb2c1

verified ·

1 Parent(s): 5bc32b2

Link the published paper

Browse files

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README.md +2 -0

README.md CHANGED Viewed

@@ -19,6 +19,8 @@ tags:
 A 1.5B model that compresses English reasoning text into a telegraphic CJK/symbol register under deterministic fidelity gates. It minted the training data for [Tessera-Preview-9B](https://huggingface.co/ZelligeAI/tessera-preview-9b) and replaces the frontier-model teacher that originally produced the register: English reasoning text becomes compressed-register training data at local-inference cost, with no API key and no external dependency. Validation covered code-centric reasoning (103 held-out mixed blocks); behavior on distant domains is unmeasured.
 Example (real training pair, 85 to 49 tokens):
 ```text

 A 1.5B model that compresses English reasoning text into a telegraphic CJK/symbol register under deterministic fidelity gates. It minted the training data for [Tessera-Preview-9B](https://huggingface.co/ZelligeAI/tessera-preview-9b) and replaces the frontier-model teacher that originally produced the register: English reasoning text becomes compressed-register training data at local-inference cost, with no API key and no external dependency. Validation covered code-centric reasoning (103 held-out mixed blocks); behavior on distant domains is unmeasured.
+**Paper:** [Tessera-Preview-9B: Compressed Reasoning at 18x Fewer Tokens, and What It Costs](https://zellige.ai/research/compressed-cjk-reasoning) — section 3.1 covers this compressor's design and acceptance record.
 Example (real training pair, 85 to 49 tokens):
 ```text