Instructions to use crumb/doc2desc_3b_gguf 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 crumb/doc2desc_3b_gguf 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 crumb/doc2desc_3b_gguf:BF16 # Run inference directly in the terminal: llama cli -hf crumb/doc2desc_3b_gguf:BF16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf crumb/doc2desc_3b_gguf:BF16 # Run inference directly in the terminal: llama cli -hf crumb/doc2desc_3b_gguf:BF16
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 crumb/doc2desc_3b_gguf:BF16 # Run inference directly in the terminal: ./llama-cli -hf crumb/doc2desc_3b_gguf:BF16
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 crumb/doc2desc_3b_gguf:BF16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf crumb/doc2desc_3b_gguf:BF16
Use Docker
docker model run hf.co/crumb/doc2desc_3b_gguf:BF16
- LM Studio
- Jan
- Ollama
How to use crumb/doc2desc_3b_gguf with Ollama:
ollama run hf.co/crumb/doc2desc_3b_gguf:BF16
- Unsloth Desktop
- Pi
How to use crumb/doc2desc_3b_gguf with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf crumb/doc2desc_3b_gguf:BF16
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "crumb/doc2desc_3b_gguf:BF16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use crumb/doc2desc_3b_gguf with Docker Model Runner:
docker model run hf.co/crumb/doc2desc_3b_gguf:BF16
- Lemonade
How to use crumb/doc2desc_3b_gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull crumb/doc2desc_3b_gguf:BF16
Run and chat with the model
lemonade run user.doc2desc_3b_gguf-BF16
List all available models
lemonade list
- Hermes Agent
How to use crumb/doc2desc_3b_gguf with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf crumb/doc2desc_3b_gguf:BF16
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 crumb/doc2desc_3b_gguf:BF16
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use crumb/doc2desc_3b_gguf with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf crumb/doc2desc_3b_gguf:BF16
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 "crumb/doc2desc_3b_gguf:BF16" \ --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"
DOC2DESC 3B
This is Qwen/Qwen2.5-3B tuned with the following format, on a mix of handwritten and Deepseek-V3 generated descriptions (few-shot w/ handwritten descriptions) for texts from https://textfiles.com, to make sure it has the ability to label unsafe content. This is being used to generate heaps of description/document pairs for training another model to do the reverse, for automatically generating documents to create control-vectors from.
Context format
During training it saw this format:
[[DOCUMENT]]{document}[[/DOCUMENT]]
[[DESCRIPTION]]{description}[[/DESCRIPTION]]
| Position | Delimiter |
|---|---|
| before user | [[DOCUMENT]] |
| after user | [[/DOCUMENT]] |
| before assistant | [[DESCRIPTION]] |
| after assistant | [[/DESCRIPTION]] |
you may also want to add "[[" as a stop string.. light tune, isn't perfect ๐
Output qualities
The outputs are something like informal summaries, for example, on the first element from the C4 dataset here are some outputs (at temperature 0.8):
(text from C4):
Beginners BBQ Class Taking Place in Missoula!
Do you want to get better at making delicious BBQ? You will have the opportunity, put this on your calendar now. Thursday, September 22nd join World Class BBQ Champion, Tony Balay from Lonestar Smoke Rangers. He will be teaching a beginner level class for everyone who wants to get better with their culinary skills.
He will teach you everything you need to know to compete in a KCBS BBQ competition, including techniques, recipes, timelines, meat selection and trimming, plus smoker and fire information.
The cost to be in the class is $35 per person, and for spectators it is free. Included in the cost will be either a t-shirt or apron and you will be tasting samples of each meat that is prepared.
(outputs):
ad to get better at making delicious BBQ by world class bbq champion from lonestar smoke rangers.
ad for BBQ class at lonestar smoke rangers by world class bbq champ tony balay; includes techniques, recipes
event ad: beginners BBQ Class Taking Place in Missoula! from world class bbs champion tony balay
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Model tree for crumb/doc2desc_3b_gguf
Base model
Qwen/Qwen2.5-3B