BitAgent-8B

BitAgent-8B is an open-source, tool-calling language model fine-tuned and incentivized on Bittensor Subnet #20 -- BitAgent. This model was trained for complex function-calling tasks, drawing on decentralized AI efforts and an open, community-driven training approach.

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Overview

BitAgent-8B arose from the collaborative efforts within Bittensor Subnet #20. It leverages:

• Decentralized AI: Community-driven hosting and validation that provide continuous training signals, ensuring that the model adapts to a wide range of function-calling and workflow-building tasks.
• Previous Dataset Top Performer: The model’s parameters have been refined in multiple training competitions, this specific model having done well several rounds ago against tool_shuffle_small round 1, our constantly evolving task set.
• Broad Tool-Calling Agency: It handles a diverse set of functions spanning financial calculations, workflow management, deployment scripts, and more.

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Key Features

• Enhanced Tool Usage: Fine-tuned to select, utilize, and chain functions from a toolset effectively.
• BFCL-Style Adherence: Engineered for strong performance on the Berkeley Function Calling Leaderboard.
• Decentralized & Community Driven: Developed and hosted on a global, miner-supported network (Subnet #20 on Bittensor), encouraging open contribution and verifying model performance without reliance on centralized resources.

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Performance on BFCL

BitAgent-8B secured a top-10 rank (6th place) on the BFCL, ahead of notable commercial models such as:

• 4o Mini
• Gemini
• Qwen
• DeepSeek
• Claude

While some high-ranking small-form models on BFCL may have overfit specific function-calling tasks, BitAgent-8B was purposefully trained to preserve broad generalization. This emphasis on diverse tasks ensures robust, consistent performance across a variety of real-world use cases.

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Open-Source Incentive Training

BitAgent-8B was developed with an incentive mechanism on Bittensor Subnet #20:

• Miners contributed compute to fine-tune and host candidate models.
• Validators continuously tested these models on domain-specific tool-calling prompts.
• Incentive mechanism drove iterative improvements with unique challenges targetting at combating overfitting.

This ecosystem ensures BitAgent-8B remains:

1. Adaptable to new tasks.
2. Decentralized in design, with no single entity controlling its training pipeline.
3. Transparent in performance metrics, with all scoring data publicly available on the BFCL.

For additional background on Subnet #20 and details about the training setup, see the Subnet 20 Readme (or refer to the documentation you already have).

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Installation & Usage

Below is a minimal example of loading BitAgent-8B using Hugging Face transformers. Adjust paths and parameters according to your environment:

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "BitAgent/Bitagent-8b"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto"
)

# Example usage in Python
# (Please adapt to your environment or use Bittensor's APIs accordingly)
prompt = """You are an expert in function calling. 
You are given a question and a set of possible tools. 
You must decide if a tool should be invoked. 
Format tool calls strictly as: [tool_name(param=value, param2=value2)] 
If no tool is relevant or required parameters are missing, please respond that the request can't be fulfilled."""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(inputs["input_ids"], max_new_tokens=100, do_sample=False)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Sample Prompt & Invocation Structure

Following the BFCL-style function invocation structure, BitAgent-8B expects prompts that look like this:

Prompt Template

"You are an expert in composing functions. 
You are given a question and a set of possible functions. 
Based on the question, you will need to make one or more function/tool calls to achieve the purpose.
If none of the functions can be used, point it out. 
If the given question lacks the parameters required by any function, also point it out.
You should only return the function call in tools call sections.

If you decide to invoke any of the function(s), 
you MUST put it in the format of [func_name(params_name1=params_value1, params_name2=params_value2...)]. 
You SHOULD NOT include any other text in the response.
Here is a list of functions in JSON format that you can invoke:

{functions}"

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License

BitAgent-8B is open-sourced under the Apache 2.0 License