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Flexan/
Blake-XTM-Arc-T0-GGUF

Name: Blake-XTM-Arc-T0-GGUF
Availability: InStock

Flexan/Blake-XTM-Arc-T0

quantized

Jofthomas/hermes-function-calling-thinking-V1

PJMixers-Dev/dolphin-deepseek-1k-think-1k-response-filtered-ShareGPT

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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.:

text

<|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:
text
1<|im_start|>assistant 2Hello! How may I assist you today?<|im_end|>
Tool call:
text
1<|im_start|>assistant 2<|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:
text
1You are an advanced AI model with tool-calling capabilities. 2 3If the user asks something and it requires a tool then you should call the tool with the arguments. 4 5# Tools 6You have access to the following tools: 7[{'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': []}}}] 8 9To 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|>

Flexan/Blake-XTM-Arc-T0-GGUF