Instructions to use rovdetection/code-1b-chat-v2-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rovdetection/code-1b-chat-v2-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="rovdetection/code-1b-chat-v2-gguf",
	filename="code-1b-chat-v2-Q4_K_M.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use rovdetection/code-1b-chat-v2-gguf with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf rovdetection/code-1b-chat-v2-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf rovdetection/code-1b-chat-v2-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf rovdetection/code-1b-chat-v2-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf rovdetection/code-1b-chat-v2-gguf:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf rovdetection/code-1b-chat-v2-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf rovdetection/code-1b-chat-v2-gguf:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf rovdetection/code-1b-chat-v2-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf rovdetection/code-1b-chat-v2-gguf:Q4_K_M

Use Docker

docker model run hf.co/rovdetection/code-1b-chat-v2-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use rovdetection/code-1b-chat-v2-gguf with Ollama:
```
ollama run hf.co/rovdetection/code-1b-chat-v2-gguf:Q4_K_M
```

Unsloth Studio

How to use rovdetection/code-1b-chat-v2-gguf with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for rovdetection/code-1b-chat-v2-gguf to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for rovdetection/code-1b-chat-v2-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for rovdetection/code-1b-chat-v2-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use rovdetection/code-1b-chat-v2-gguf with Docker Model Runner:
```
docker model run hf.co/rovdetection/code-1b-chat-v2-gguf:Q4_K_M
```

Lemonade

How to use rovdetection/code-1b-chat-v2-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull rovdetection/code-1b-chat-v2-gguf:Q4_K_M

Run and chat with the model

lemonade run user.code-1b-chat-v2-gguf-Q4_K_M

List all available models

lemonade list

code-1b-chat-v2-gguf / README.md

rovdetection

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	---
	license: apache-2.0
	tags:
	- gguf
	- code
	- llama-cpp
	base_model: rovdetection/code-1b-chat-v2
	---

	# code-1b-chat-v2 GGUF

	GGUF quantization of [rovdetection/code-1b-chat-v2](https://huggingface.co/rovdetection/code-1b-chat-v2).

	## Available files

	\| File \| Quant \| Size \| Use case \|
	\|------\|-------\|------\|----------\|
	\| `code-1b-chat-v2-Q4_K_M.gguf` \| Q4_K_M \| ~700 MB \| Recommended — best quality/speed \|

	## Usage with llama-cpp-python

	```python
	from llama_cpp import Llama

	llm = Llama.from_pretrained(
	repo_id="rovdetection/code-1b-chat-v2-gguf",
	filename="code-1b-chat-v2-Q4_K_M.gguf",
	n_ctx=2048,
	)

	SYSTEM = "Below is an instruction that describes a coding task. Write a response that appropriately completes the request."
	prompt = f"{SYSTEM}\n\n### Instruction:\nWrite a fibonacci function.\n\n### Response:\n"

	out = llm(prompt, max_tokens=200, temperature=0.7, stop=["### Instruction:"])
	print(out["choices"][0]["text"])
	```