Instructions to use Flexan/Blake-XTM-Arc-T0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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 Settings

llama.cpp

How to use Flexan/Blake-XTM-Arc-T0-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 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 serve -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

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

Atomic Chat new
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

Flexan commited on Jun 14, 2025

Commit

61945db

verified ·

1 Parent(s): c11dffe

Fixed README

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -82,7 +82,7 @@ We recommend using the following system prompts for your situation:
   # 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': []}}}] <\/tools>Use the following pydantic model json schema for each tool call you will make: {'title': 'FunctionCall', 'type': 'object', 'properties': {'arguments': {'title': 'Arguments', 'type': 'object'}, 'name': {'title': 'Name', 'type': 'string'}}, 'required': ['arguments', 'name']}
   To call a tool, write a JSON object with the name and arguments inside <|tool_start|>...<|tool_end|>.
   ```

   # 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|>.
   ```