Instructions to use Flexan/Blake-XTM-Arc-T0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Flexan/Blake-XTM-Arc-T0-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Flexan/Blake-XTM-Arc-T0-GGUF", filename="Blake-XTM-Arc-T0.IQ3_M.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use Flexan/Blake-XTM-Arc-T0-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Flexan/Blake-XTM-Arc-T0-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Flexan/Blake-XTM-Arc-T0-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Flexan/Blake-XTM-Arc-T0-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Flexan/Blake-XTM-Arc-T0-GGUF:Q4_K_M
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 Flexan/Blake-XTM-Arc-T0-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Flexan/Blake-XTM-Arc-T0-GGUF:Q4_K_M
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 Flexan/Blake-XTM-Arc-T0-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Flexan/Blake-XTM-Arc-T0-GGUF:Q4_K_M
Use Docker
docker model run hf.co/Flexan/Blake-XTM-Arc-T0-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Flexan/Blake-XTM-Arc-T0-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Flexan/Blake-XTM-Arc-T0-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Flexan/Blake-XTM-Arc-T0-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Flexan/Blake-XTM-Arc-T0-GGUF:Q4_K_M
- Ollama
How to use Flexan/Blake-XTM-Arc-T0-GGUF with Ollama:
ollama run hf.co/Flexan/Blake-XTM-Arc-T0-GGUF:Q4_K_M
- Unsloth Studio new
How to use Flexan/Blake-XTM-Arc-T0-GGUF 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 Flexan/Blake-XTM-Arc-T0-GGUF 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 Flexan/Blake-XTM-Arc-T0-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Flexan/Blake-XTM-Arc-T0-GGUF to start chatting
- Docker Model Runner
How to use Flexan/Blake-XTM-Arc-T0-GGUF with Docker Model Runner:
docker model run hf.co/Flexan/Blake-XTM-Arc-T0-GGUF:Q4_K_M
- Lemonade
How to use Flexan/Blake-XTM-Arc-T0-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Flexan/Blake-XTM-Arc-T0-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Blake-XTM-Arc-T0-GGUF-Q4_K_M
List all available models
lemonade list
GGUF Files for Blake-XTM-Arc-T0
These are the GGUF files for Flexan/Blake-XTM-Arc-T0.
| GGUF Link | Quantization | Description |
|---|---|---|
| Download | Q2_K | Lowest quality |
| Download | IQ3_XS | Integer quant |
| Download | Q3_K_S | |
| Download | IQ3_S | Integer quant, preferable over Q3_K_S |
| Download | IQ3_M | Integer quant |
| Download | Q3_K_M | |
| Download | Q3_K_L | |
| Download | IQ4_XS | Integer quant |
| Download | Q4_K_S | Fast with good performance |
| Download | Q4_K_M | Recommended: Perfect mix of speed and performance |
| Download | Q5_K_S | |
| Download | Q5_K_M | |
| Download | Q6_K | Very good quality |
| Download | Q8_0 | Best quality |
| Download | f16 | Full precision, don't bother; use a quant |
Model Card for Blake-XTM Arc T0
Blake-XTM Arc T0 is a 7B large language model used for text generation. It was trained as a (tool-calling) assistant. This model is a variation of Blake-XTM-Arc without reasoning.
Model Details
Model Description
Blake-XTM Arc T0 is a 7B parameter instruct LLM trained to assist and optionally call a tool. It only supports using one tool per assistant message (no parallel tool calling). The model was LoRA fine-tuned with CatNyanster-7B as base model, which was fine-tuned on Mistral-7B.
Chat Format
Blake-XTM Arc T0 uses the ChatML format, e.g.:
<|im_start|>system
System message<|im_end|>
<|im_start|>user
User prompt<|im_end|>
<|im_start|>assistant
Assistant response<|im_end|>
Model Usage
The assistant response can have the following two formats (the contents are examples and were not generated from the model):
- Response:
<|im_start|>assistant Hello! How may I assist you today?<|im_end|> - Tool call:
<|im_start|>assistant <|tool_start|>{'name': 'find_restaurants', 'arguments': {'city': 'Paris', 'country': 'France'}}<|tool_end|><|im_end|>
We recommend using the following system prompts for your situation:
- Only thought process:
You are an advanced AI model. - Thought process and tool calling:
You are an advanced AI model with tool-calling capabilities. If the user asks something and it requires a tool then you should call the tool with the arguments. # Tools You have access to the following tools: [{'type': 'function', 'function': {'name': 'convert_currency', 'description': 'Convert currency from one type to another', 'parameters': {'type': 'object', 'properties': {'amount': {'type': 'number', 'description': 'The amount to be converted'}, 'from_currency': {'type': 'string', 'description': 'The currency to convert from'}, 'to_currency': {'type': 'string', 'description': 'The currency to convert to'}}, 'required': ['amount', 'from_currency', 'to_currency']}}}, {'type': 'function', 'function': {'name': 'get_random_joke', 'description': 'Get a random joke', 'parameters': {'type': 'object', 'properties': {}, 'required': []}}}] To call a tool, write a JSON object with the name and arguments inside <|tool_start|>...<|tool_end|>.
For responding with a tool response, you can send a message as the tool user:
<|im_start|>assistant
<|tool_start|>{'name': 'find_restaurants', 'arguments': {'city': 'Paris', 'country': 'France'}}<|tool_end|><|im_end|>
<|im_start|>tool
{'restaurants': [{'name': 'A Restaurant Name', 'rating': 4.5}]}<|im_end|>
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