Instructions to use Bob-the-Koala/LocalCodeViber with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Bob-the-Koala/LocalCodeViber with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Bob-the-Koala/LocalCodeViber")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Bob-the-Koala/LocalCodeViber")
model = AutoModelForCausalLM.from_pretrained("Bob-the-Koala/LocalCodeViber", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Bob-the-Koala/LocalCodeViber with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Bob-the-Koala/LocalCodeViber"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Bob-the-Koala/LocalCodeViber",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Bob-the-Koala/LocalCodeViber

SGLang

How to use Bob-the-Koala/LocalCodeViber with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Bob-the-Koala/LocalCodeViber" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Bob-the-Koala/LocalCodeViber",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Bob-the-Koala/LocalCodeViber" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Bob-the-Koala/LocalCodeViber",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Bob-the-Koala/LocalCodeViber with Docker Model Runner:
```
docker model run hf.co/Bob-the-Koala/LocalCodeViber
```

LocalCodeViber / README.md

Bob-the-Koala

Update README.md

e4cd9d6 verified 5 months ago

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	---
	base_model: unsloth/Qwen3-8B-Base-unsloth-bnb-4bit
	tags:
	- transformers
	- qwen3
	- Unsloth
	- code
	- agent
	- Fine-tune
	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

	LocalCodeViber v0.1 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.

	This is the SFT foundation model. Reinforcement learning is ongoing.

	---

	## 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

	---

	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)