Instructions to use CISCai/OpenCodeInterpreter-DS-6.7B-SOTA-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CISCai/OpenCodeInterpreter-DS-6.7B-SOTA-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="CISCai/OpenCodeInterpreter-DS-6.7B-SOTA-GGUF",
	filename="OpenCodeInterpreter-DS-6.7B.IQ1_S.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 CISCai/OpenCodeInterpreter-DS-6.7B-SOTA-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf CISCai/OpenCodeInterpreter-DS-6.7B-SOTA-GGUF:IQ1_S
# Run inference directly in the terminal:
llama-cli -hf CISCai/OpenCodeInterpreter-DS-6.7B-SOTA-GGUF:IQ1_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf CISCai/OpenCodeInterpreter-DS-6.7B-SOTA-GGUF:IQ1_S
# Run inference directly in the terminal:
llama-cli -hf CISCai/OpenCodeInterpreter-DS-6.7B-SOTA-GGUF:IQ1_S

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 CISCai/OpenCodeInterpreter-DS-6.7B-SOTA-GGUF:IQ1_S
# Run inference directly in the terminal:
./llama-cli -hf CISCai/OpenCodeInterpreter-DS-6.7B-SOTA-GGUF:IQ1_S

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 CISCai/OpenCodeInterpreter-DS-6.7B-SOTA-GGUF:IQ1_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf CISCai/OpenCodeInterpreter-DS-6.7B-SOTA-GGUF:IQ1_S

Use Docker

docker model run hf.co/CISCai/OpenCodeInterpreter-DS-6.7B-SOTA-GGUF:IQ1_S

LM Studio
Jan

vLLM

How to use CISCai/OpenCodeInterpreter-DS-6.7B-SOTA-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CISCai/OpenCodeInterpreter-DS-6.7B-SOTA-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": "CISCai/OpenCodeInterpreter-DS-6.7B-SOTA-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CISCai/OpenCodeInterpreter-DS-6.7B-SOTA-GGUF:IQ1_S

Ollama
How to use CISCai/OpenCodeInterpreter-DS-6.7B-SOTA-GGUF with Ollama:
```
ollama run hf.co/CISCai/OpenCodeInterpreter-DS-6.7B-SOTA-GGUF:IQ1_S
```

Unsloth Studio

How to use CISCai/OpenCodeInterpreter-DS-6.7B-SOTA-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 CISCai/OpenCodeInterpreter-DS-6.7B-SOTA-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 CISCai/OpenCodeInterpreter-DS-6.7B-SOTA-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for CISCai/OpenCodeInterpreter-DS-6.7B-SOTA-GGUF to start chatting

Docker Model Runner
How to use CISCai/OpenCodeInterpreter-DS-6.7B-SOTA-GGUF with Docker Model Runner:
```
docker model run hf.co/CISCai/OpenCodeInterpreter-DS-6.7B-SOTA-GGUF:IQ1_S
```

Lemonade

How to use CISCai/OpenCodeInterpreter-DS-6.7B-SOTA-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull CISCai/OpenCodeInterpreter-DS-6.7B-SOTA-GGUF:IQ1_S

Run and chat with the model

lemonade run user.OpenCodeInterpreter-DS-6.7B-SOTA-GGUF-IQ1_S

List all available models

lemonade list

CISCai commited on Feb 26, 2024

Commit

a24bb6a

verified ·

1 Parent(s): d78cfeb

Upload 3 files

Browse files

Requantized IQ1_S with a 4K-context imatrix.

Files changed (4) hide show

.gitattributes +1 -0
OpenCodeInterpreter-DS-6.7B.IQ1_S.gguf +1 -1
OpenCodeInterpreter-DS-6.7B.imatrix-4096.dat +3 -0
README.md +2 -1

.gitattributes CHANGED Viewed

@@ -43,3 +43,4 @@ OpenCodeInterpreter-DS-6.7B.IQ3_M.gguf filter=lfs diff=lfs merge=lfs -text
 OpenCodeInterpreter-DS-6.7B.IQ3_S.gguf filter=lfs diff=lfs merge=lfs -text
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 OpenCodeInterpreter-DS-6.7B.IQ3_XXS.gguf filter=lfs diff=lfs merge=lfs -text

 OpenCodeInterpreter-DS-6.7B.IQ3_S.gguf filter=lfs diff=lfs merge=lfs -text
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 OpenCodeInterpreter-DS-6.7B.IQ3_XXS.gguf filter=lfs diff=lfs merge=lfs -text
+OpenCodeInterpreter-DS-6.7B.imatrix-4096.dat filter=lfs diff=lfs merge=lfs -text

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README.md CHANGED Viewed

@@ -25,7 +25,7 @@ This repo contains State Of The Art quantized GGUF format model files for [OpenC
 Quantization was done with an importance matrix that was trained for ~1M tokens (2000 batches of 512 tokens) of answers from the [CodeFeedback-Filtered-Instruction](https://huggingface.co/datasets/m-a-p/CodeFeedback-Filtered-Instruction) dataset.
-Even though the 1-bit quantized model file "works" it is **not recommended** for normal use as it is extremely error-prone and pretty much defaults to infinite loops, you have been warned. 🧐
 <!-- description end -->
@@ -88,6 +88,7 @@ Refer to the Provided Files table below to see what files use which methods, and
 | [OpenCodeInterpreter-DS-6.7B.IQ3_M.gguf](https://huggingface.co/CISCai/OpenCodeInterpreter-DS-6.7B-SOTA-GGUF/blob/main/OpenCodeInterpreter-DS-6.7B.IQ3_M.gguf) | IQ3_M | 3 | 3.0 GB| 5.0 GB | medium, balanced quality - recommended |
 Generated importance matrix file: [OpenCodeInterpreter-DS-6.7B.imatrix.dat](https://huggingface.co/CISCai/OpenCodeInterpreter-DS-6.7B-SOTA-GGUF/blob/main/OpenCodeInterpreter-DS-6.7B.imatrix.dat)
 **Note**: the above RAM figures assume no GPU offloading with 4K context. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.

 Quantization was done with an importance matrix that was trained for ~1M tokens (2000 batches of 512 tokens) of answers from the [CodeFeedback-Filtered-Instruction](https://huggingface.co/datasets/m-a-p/CodeFeedback-Filtered-Instruction) dataset.
+Even though the 1-bit quantized model file "works" it is **not recommended** for normal use ~~as it is extremely error-prone~~, I've requantized it with a 4K-context imatrix which seems to have improved it a little bit but it still defaults to infinite loops, you have been warned. 🧐
 <!-- description end -->
 | [OpenCodeInterpreter-DS-6.7B.IQ3_M.gguf](https://huggingface.co/CISCai/OpenCodeInterpreter-DS-6.7B-SOTA-GGUF/blob/main/OpenCodeInterpreter-DS-6.7B.IQ3_M.gguf) | IQ3_M | 3 | 3.0 GB| 5.0 GB | medium, balanced quality - recommended |
 Generated importance matrix file: [OpenCodeInterpreter-DS-6.7B.imatrix.dat](https://huggingface.co/CISCai/OpenCodeInterpreter-DS-6.7B-SOTA-GGUF/blob/main/OpenCodeInterpreter-DS-6.7B.imatrix.dat)
+Generated importance matrix file (4K context): [OpenCodeInterpreter-DS-6.7B.imatrix-4096.dat](https://huggingface.co/CISCai/OpenCodeInterpreter-DS-6.7B-SOTA-GGUF/blob/main/OpenCodeInterpreter-DS-6.7B.imatrix-4096.dat)
 **Note**: the above RAM figures assume no GPU offloading with 4K context. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.