Instructions to use dranger003/OpenCodeInterpreter-CL-70B-iMat.GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dranger003/OpenCodeInterpreter-CL-70B-iMat.GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="dranger003/OpenCodeInterpreter-CL-70B-iMat.GGUF",
	filename="ggml-opencodeinterpreter-cl-70b-iq2_m.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use dranger003/OpenCodeInterpreter-CL-70B-iMat.GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf dranger003/OpenCodeInterpreter-CL-70B-iMat.GGUF:IQ2_M
# Run inference directly in the terminal:
llama-cli -hf dranger003/OpenCodeInterpreter-CL-70B-iMat.GGUF:IQ2_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf dranger003/OpenCodeInterpreter-CL-70B-iMat.GGUF:IQ2_M
# Run inference directly in the terminal:
llama-cli -hf dranger003/OpenCodeInterpreter-CL-70B-iMat.GGUF:IQ2_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 dranger003/OpenCodeInterpreter-CL-70B-iMat.GGUF:IQ2_M
# Run inference directly in the terminal:
./llama-cli -hf dranger003/OpenCodeInterpreter-CL-70B-iMat.GGUF:IQ2_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 dranger003/OpenCodeInterpreter-CL-70B-iMat.GGUF:IQ2_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dranger003/OpenCodeInterpreter-CL-70B-iMat.GGUF:IQ2_M

Use Docker

docker model run hf.co/dranger003/OpenCodeInterpreter-CL-70B-iMat.GGUF:IQ2_M

LM Studio
Jan

vLLM

How to use dranger003/OpenCodeInterpreter-CL-70B-iMat.GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dranger003/OpenCodeInterpreter-CL-70B-iMat.GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dranger003/OpenCodeInterpreter-CL-70B-iMat.GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/dranger003/OpenCodeInterpreter-CL-70B-iMat.GGUF:IQ2_M

Ollama
How to use dranger003/OpenCodeInterpreter-CL-70B-iMat.GGUF with Ollama:
```
ollama run hf.co/dranger003/OpenCodeInterpreter-CL-70B-iMat.GGUF:IQ2_M
```

Unsloth Studio new

How to use dranger003/OpenCodeInterpreter-CL-70B-iMat.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 dranger003/OpenCodeInterpreter-CL-70B-iMat.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 dranger003/OpenCodeInterpreter-CL-70B-iMat.GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for dranger003/OpenCodeInterpreter-CL-70B-iMat.GGUF to start chatting

Docker Model Runner
How to use dranger003/OpenCodeInterpreter-CL-70B-iMat.GGUF with Docker Model Runner:
```
docker model run hf.co/dranger003/OpenCodeInterpreter-CL-70B-iMat.GGUF:IQ2_M
```

Lemonade

How to use dranger003/OpenCodeInterpreter-CL-70B-iMat.GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull dranger003/OpenCodeInterpreter-CL-70B-iMat.GGUF:IQ2_M

Run and chat with the model

lemonade run user.OpenCodeInterpreter-CL-70B-iMat.GGUF-IQ2_M

List all available models

lemonade list

NOTE: You will need a recent build of llama.cpp to run these quants (i.e. at least commit 494c870).

GGUF importance matrix (imatrix) quants for https://huggingface.co/m-a-p/OpenCodeInterpreter-CL-70B

The importance matrix was trained for ~50K tokens (105 batches of 512 tokens) using a general purpose imatrix calibration dataset.
The imatrix is being used on the K-quants as well.

OpenCodeInterpreter is a family of open-source code generation systems designed to bridge the gap between large language models and advanced proprietary systems like the GPT-4 Code Interpreter. It significantly advances code generation capabilities by integrating execution and iterative refinement functionalities. This model is based on CodeLlama-70b-Python-hf.

Layers	Context	Template
80	16384	<s>[INST] {prompt} [/INST]

Downloads last month: 13

GGUF

Model size

69B params

Architecture

llama

Hardware compatibility

2-bit