Instructions to use bartowski/AutoCoder-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bartowski/AutoCoder-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="bartowski/AutoCoder-GGUF",
	filename="AutoCoder-IQ2_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use bartowski/AutoCoder-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 bartowski/AutoCoder-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bartowski/AutoCoder-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 bartowski/AutoCoder-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bartowski/AutoCoder-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 bartowski/AutoCoder-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf bartowski/AutoCoder-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 bartowski/AutoCoder-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bartowski/AutoCoder-GGUF:Q4_K_M

Use Docker

docker model run hf.co/bartowski/AutoCoder-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use bartowski/AutoCoder-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/bartowski/AutoCoder-GGUF:Q4_K_M

Ollama
How to use bartowski/AutoCoder-GGUF with Ollama:
```
ollama run hf.co/bartowski/AutoCoder-GGUF:Q4_K_M
```

Unsloth Studio

How to use bartowski/AutoCoder-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 bartowski/AutoCoder-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 bartowski/AutoCoder-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for bartowski/AutoCoder-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use bartowski/AutoCoder-GGUF with Docker Model Runner:
```
docker model run hf.co/bartowski/AutoCoder-GGUF:Q4_K_M
```

Lemonade

How to use bartowski/AutoCoder-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bartowski/AutoCoder-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.AutoCoder-GGUF-Q4_K_M

List all available models

lemonade list

prompt format and text-gen

by gandolfi - opened Jun 6, 2024

Discussion

gandolfi

Jun 6, 2024

hello,
how i can configure this prompt format in text-gen ?

{system_prompt}
Human: {prompt}
Assistant: <|EOT|>

thanks

bartowski

Owner Jun 6, 2024

It should work yes, was extracted from the model's chat template

gandolfi

Jun 6, 2024

thanks. When i use text-gen oobabooga . i just have "point" for answer.

https://cdn.discordapp.com/attachments/1089972954353369151/1248248126750593065/image.png?ex=6662f928&is=6661a7a8&hm=07d691722d9f5d317adf055b3f766eb04f603eccafd05723e854a47a2c845565&

bartowski

Owner Jun 6, 2024

Try instruct instead of chat instruct, I personally dislike chat instruct cause it wraps everything in "the following is a chat between a user and a bot" or something..

Your rope frequency also looks off, try setting it to 4 for I think alpha_value (may be the compress one), I've found even when not pushing context some models NEED the rope frequency or they become incoherent

gandolfi

Jun 6, 2024

i have try with instruct only and alpha value to 4. but still incoherent. i will try with ollama.

cgus

Jun 7, 2024

AutoCoder models are based on DeepSeek-Coder that needs to use compress_emb_pos = 4 to extend context from 4k to 16k.
It seems TGW properly reads GGUF rope scaling from metadata from old DeepSeek-Coder models (e.g. TheBloke's) but doesn't recognize it in newer ones.
Most likely GGUF metadata format changed since then and this change wasn't reflected in TGW code where it only recognizes old "rope.scale_linear" entry but in newer GGUFs it's defined in two entries which it doesn't check: "rope.scaling.factor" and "rope.scaling.type".

Imho, since the model was trained with linear scaling, most likely compress_emb_pos should work better here than alpha value.

bartowski

Owner Jun 8, 2024

Thanks @cgus I couldn't remember which was correct, and your comment seems likely to be true for the rest as well!

gandolfi

Jun 8, 2024

thanks. Works now with compress_emb_pos = 4 and an update of text-gen

bartowski

Owner Jun 8, 2024

Awesome glad to hear it!!

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