Update README.md

README.md

@@ -8,14 +8,149 @@ tags:
 license: apache-2.0
 language:
 - en
+datasets:
+- TeichAI/MiniMax-M2.1-Code-SFT
+- TeichAI/MiniMax-M2.1-8800x
+- TeichAI/convo-v1
+- AlicanKiraz0/Agentic-Chain-of-Thought-Coding-SFT-Dataset-v1.1
+- TeichAI/claude-4.5-opus-high-reasoning-250x
+pipeline_tag: text-generation
 ---
+# LocalCodeViber
 
-# Uploaded finetuned  model
+**LocalCodeViber** is a local-first agentic coding model built on [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B), fine-tuned for tool-calling, multi-step code generation, and autonomous error recovery. Designed to run entirely on consumer hardware — no API, no cloud, no cost per token.
 
-- **Developed by:** Bob-the-Koala
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/Qwen3-8B-Base-unsloth-bnb-4bit
+This is the SFT foundation model. Reinforcement learning is ongoing.
 
-This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+---
+
+## What it does
+
+LocalCodeViber was trained to operate as a coding agent — not just generate code, but use tools to read files, write files, run commands, search the web, and recover from failures just like a real developer would.
+
+It can:
+
+- Read and edit files in a workspace
+- Write complete, working code from a single prompt
+- Execute shell commands and interpret the output
+- Recover from failed tool calls without giving up
+- Create pull requests on GitHub repositories
+- Think through problems step by step using native `<think>` tags before acting
+
+---
+
+## Model Details
+
+| | |
+|---|---|
+| **Base Model** | Qwen3-8B-Base |
+| **Architecture** | Qwen3 transformer, 36 layers |
+
+## Training Data
+
+LocalCodeViber was trained on a curated mix of 14,837 examples across 5 datasets:
+
+| Dataset | Examples | Focus |
+|---|---|---|
+| [TeichAI/convo-v1](https://huggingface.co/datasets/TeichAI/convo-v1) | 777 | Conversational format, instruction following |
+| [AlicanKiraz0/Agentic-Chain-of-Thought-Coding-SFT-Dataset-v1.1](https://huggingface.co/datasets/AlicanKiraz0/Agentic-Chain-of-Thought-Coding-SFT-Dataset-v1.1) | ~3,700 | Agentic reasoning and tool use |
+| [TeichAI/MiniMax-M2.1-Code-SFT](https://huggingface.co/datasets/TeichAI/MiniMax-M2.1-Code-SFT) | ~1,300 | Agentic Code generation |
+| [TeichAI/MiniMax-M2.1-8800x](https://huggingface.co/datasets/TeichAI/MiniMax-M2.1-8800x) | 8,800 | Diverse coding tasks |
+| [TeichAI/claude-4.5-opus-high-reasoning-250x](https://huggingface.co/datasets/TeichAI/claude-4.5-opus-high-reasoning-250x) | 250 | High-quality reasoning traces |
+
+The dataset mix emphasises real agentic tool-use patterns including failed tool calls that are identified, diagnosed, and corrected — giving the model genuine error recovery capability rather than just pattern matching on success cases.
+
+---
+
+## Tools
+
+LocalCodeViber understands the following tool schema out of the box:
+
+```json
+["read_file", "write_file", "edit_file", "list_directory", "search_code", "run_command", "web_search"]
+```
+
+These match the tools in the training data. Pass them via the standard OpenAI tool calling API.
+
+---
+
+## Usage
+
+### LM Studio (Recommended)
+
+1. Download the GGUF version: [Bob-the-Koala/LocalCodeViber-GGUF](https://huggingface.co/Bob-the-Koala/LocalCodeViber-GGUF)
+2. Load in LM Studio and break free from API costs!
+
+### Ollama
+
+```bash
+ollama run hf.co/Bob-the-Koala/LocalCodeViber-GGUF:Q4_K_M
+```
+
+### Transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained(
+    "Bob-the-Koala/LocalCodeViber",
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("Bob-the-Koala/LocalCodeViber")
+```
+
+---
+
+## GGUF Versions
+
+Available in [Bob-the-Koala/LocalCodeViber-GGUF](https://huggingface.co/Bob-the-Koala/LocalCodeViber-GGUF):
+
+| Quantization | Size | Use case |
+|---|---|---|
+| `Q4_K_M` | ~4.8 GB | Everyday use, best balance |
+
+---
+
+## System Prompt
+
+For best results, use this system prompt:
+
+```
+You are a helpful coding assistant with access to file operations and code analysis tools.
+Complete the user's task thoroughly and efficiently.
+When given a coding task, create working code files in the workspace.
+```
+
+---
+
+## Limitations
+
+- Base model started from bnb-4bit weights — quality ceiling is below a full precision 8B model
+- SFT only — reinforcement learning is in progress and will significantly improve reasoning quality
+- Not suitable for tasks requiring knowledge past Qwen3's training cutoff
+
+---
+
+## Roadmap
+
+- [ ] **LocalCodeViber-RL** — reinforcement learning on top of this SFT base, optimising for code correctness and task completion
+- [ ] **LocalCodeViber-Claw** — fine-tuned specifically for [OpenClaw](https://github.com/openclaw/openclaw) skill schemas, channel routing, extra safety, and memory system
+- [ ] **LocalCodeViber-14B** — same training recipe on Qwen3-14B for substantially higher capability
+
+---
+
+## Acknowledgements
+
+LocalCodeViber was trained using [Unsloth](https://github.com/unslothai/unsloth) and would not exist without the datasets provided by [TeichAI](https://huggingface.co/TeichAI) and [AlicanKiraz0](https://huggingface.co/AlicanKiraz0).
+
+---
+
+## License
+
+This model is released under the Apache 2.0 license
+
+---
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+*Built by [Bob-the-Koala](https://huggingface.co/Bob-the-Koala)*
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)