Instructions to use MoMonir/AutoCoder_S_6.7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MoMonir/AutoCoder_S_6.7B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="MoMonir/AutoCoder_S_6.7B-GGUF",
	filename="autocoder_s_6.7b-q4_k_m.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use MoMonir/AutoCoder_S_6.7B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf MoMonir/AutoCoder_S_6.7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf MoMonir/AutoCoder_S_6.7B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf MoMonir/AutoCoder_S_6.7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf MoMonir/AutoCoder_S_6.7B-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 MoMonir/AutoCoder_S_6.7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf MoMonir/AutoCoder_S_6.7B-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 MoMonir/AutoCoder_S_6.7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf MoMonir/AutoCoder_S_6.7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/MoMonir/AutoCoder_S_6.7B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use MoMonir/AutoCoder_S_6.7B-GGUF with Ollama:
```
ollama run hf.co/MoMonir/AutoCoder_S_6.7B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use MoMonir/AutoCoder_S_6.7B-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 MoMonir/AutoCoder_S_6.7B-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 MoMonir/AutoCoder_S_6.7B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for MoMonir/AutoCoder_S_6.7B-GGUF to start chatting

Docker Model Runner
How to use MoMonir/AutoCoder_S_6.7B-GGUF with Docker Model Runner:
```
docker model run hf.co/MoMonir/AutoCoder_S_6.7B-GGUF:Q4_K_M
```

Lemonade

How to use MoMonir/AutoCoder_S_6.7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull MoMonir/AutoCoder_S_6.7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.AutoCoder_S_6.7B-GGUF-Q4_K_M

List all available models

lemonade list

MoMonir/AutoCoder_S_6.7B-GGUF

This model was converted to GGUF format from Bin12345/AutoCoder_S_6.7B using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

About GGUF (TheBloke Description)

GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.

Here is an incomplete list of clients and libraries that are known to support GGUF:

llama.cpp. The source project for GGUF. Offers a CLI and a server option.
text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.
backyard.ai (Formeraly Faraday.dev), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.
ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.

Use with llama.cpp

Install llama.cpp through brew.

brew install ggerganov/ggerganov/llama.cpp

Invoke the llama.cpp server or the CLI. CLI:

llama-cli --hf-repo MoMonir/AutoCoder_S_6.7B-GGUF --model autocoder_s_6.7b-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo MoMonir/AutoCoder_S_6.7B-GGUF --model autocoder_s_6.7b-q4_k_m.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

git clone https://github.com/ggerganov/llama.cpp && \
cd llama.cpp && \
make && \
./main -m autocoder_s_6.7b-q4_k_m.gguf -n 128

Downloads last month: 25

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

4-bit

5-bit

6-bit

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