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README.md

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 license: apache-2.0
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+---
+
+# BitAgent-8B
+
+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. 
+
+---
+
+## 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.
+- **Offset by Several Competitions**: The model’s parameters have been refined in multiple training competitions, leveraging evolving multi-turn dialogue tasks.
+- **Broad Tool-Calling Agency**: It handles a diverse set of functions spanning financial calculations, workflow management, deployment scripts, and more.
+
+---
+
+## Key Features
+- **Enhanced Tool Usage**: Fine-tuned to select, utilize, and chain functions from a toolset effectively.
+- **Multi-Turn Dialogue**: Maintains context across turns, enabling complex tasks with step-by-step interactions.
+- **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.
+
+---
+
+## 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 appear to 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.
+
+---
+
+## 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.
+- Reward signals drove iterative improvements and overcame overfitting pitfalls.
+
+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](https://github.com/RogueTensor/bitagent_subnet) (or refer to the documentation you already have).
+
+---
+
+## Installation & Usage
+
+Below is a minimal example of loading **BitAgent-8B** using Hugging Face `transformers`. Adjust paths and parameters according to your environment:
+
+```python
+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}"```
+
+
+## License
+BitAgent-8B is open-sourced under the Apache 2.0 License