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| license: apache-2.0 |
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| # BitAgent-8B |
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| BitAgent-8B is an open-source, tool-calling language model fine-tuned and incentivized on [Bittensor](https://bittensor.com) 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 |
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| **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](https://gorilla.cs.berkeley.edu/leaderboard.html). |
| - **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 |
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| 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. |
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| 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. |
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| For additional background on Subnet #20 and details about the training setup, see the [Subnet 20 Readme](https://github.com/RogueTensor/bitagent_subnet) (or refer to the documentation you already have). |
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| ## Installation & Usage |
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| Below is a minimal example of loading **BitAgent-8B** using Hugging Face `transformers`. Adjust paths and parameters according to your environment: |
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| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| import torch |
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| model_id = "BitAgent/Bitagent-8b" |
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| 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.""" |
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| 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)) |
| ``` |
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| ## Sample Prompt & Invocation Structure |
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| 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. |
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| 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: |
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| {functions}" |
| ``` |
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| ## License |
| BitAgent-8B is open-sourced under the Apache 2.0 License |
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