CISCai/OpenCodeInterpreter-DS-6.7B-SOTA-GGUF

OpenCodeInterpreter DS 6.7B - SOTA GGUF

• Model creator: Multimodal Art Projection
• Original model: OpenCodeInterpreter DS 6.7B

Description

This repo contains State Of The Art quantized GGUF format model files for OpenCodeInterpreter DS 6.7B.

Quantization was done with an importance matrix that was trained for ~1M tokens (256 batches of 4096 tokens) of answers from the CodeFeedback-Filtered-Instruction dataset.

Everything has been reconverted and quantized with a new importance matrix using llama.cpp from April 29th 2024 onwards, as of commit f4ab2a4 to ensure correct pre-tokenization. The new GGUFs will work with older llama.cpp, but this may not generate correct prompt tokens, please use a recent build to ensure the best possible results!

Prompt template: DeepSeek

You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer.
### Instruction:
{prompt}
### Response:

Compatibility

These quantised GGUFv3 files are compatible with llama.cpp from February 27th 2024 onwards, as of commit 0becb22

They are also compatible with many third party UIs and libraries provided they are built using a recent llama.cpp.

Explanation of quantisation methods

Click to see details

The new methods available are:

• GGML_TYPE_IQ1_S - 1-bit quantization in super-blocks with an importance matrix applied, effectively using 1.56 bits per weight (bpw)
• GGML_TYPE_IQ1_M - 1-bit quantization in super-blocks with an importance matrix applied, effectively using 1.75 bpw
• GGML_TYPE_IQ2_XXS - 2-bit quantization in super-blocks with an importance matrix applied, effectively using 2.06 bpw
• GGML_TYPE_IQ2_XS - 2-bit quantization in super-blocks with an importance matrix applied, effectively using 2.31 bpw
• GGML_TYPE_IQ2_S - 2-bit quantization in super-blocks with an importance matrix applied, effectively using 2.5 bpw
• GGML_TYPE_IQ2_M - 2-bit quantization in super-blocks with an importance matrix applied, effectively using 2.7 bpw
• GGML_TYPE_IQ3_XXS - 3-bit quantization in super-blocks with an importance matrix applied, effectively using 3.06 bpw
• GGML_TYPE_IQ3_XS - 3-bit quantization in super-blocks with an importance matrix applied, effectively using 3.3 bpw
• GGML_TYPE_IQ3_S - 3-bit quantization in super-blocks with an importance matrix applied, effectively using 3.44 bpw
• GGML_TYPE_IQ3_M - 3-bit quantization in super-blocks with an importance matrix applied, effectively using 3.66 bpw
• GGML_TYPE_IQ4_XS - 4-bit quantization in super-blocks with an importance matrix applied, effectively using 4.25 bpw
• GGML_TYPE_IQ4_NL - 4-bit non-linearly mapped quantization with an importance matrix applied, effectively using 4.5 bpw

Refer to the Provided Files table below to see what files use which methods, and how.

Provided files

Name	Quant method	Bits	Size	Max RAM required	Use case
OpenCodeInterpreter-DS-6.7B.IQ1_S.gguf	IQ1_S	1	1.5 GB	3.5 GB	smallest, significant quality loss - TBD: Waiting for this issue to be resolved
OpenCodeInterpreter-DS-6.7B.IQ2_XXS.gguf	IQ2_XXS	2	1.8 GB	3.8 GB	very small, high quality loss
OpenCodeInterpreter-DS-6.7B.IQ2_XS.gguf	IQ2_XS	2	1.9 GB	3.9 GB	very small, high quality loss
OpenCodeInterpreter-DS-6.7B.IQ2_S.gguf	IQ2_S	2	2.1 GB	4.1 GB	small, substantial quality loss
OpenCodeInterpreter-DS-6.7B.IQ2_M.gguf	IQ2_M	2	2.2 GB	4.2 GB	small, greater quality loss
OpenCodeInterpreter-DS-6.7B.IQ3_XXS.gguf	IQ3_XXS	3	2.5 GB	4.5 GB	very small, high quality loss
OpenCodeInterpreter-DS-6.7B.IQ3_XS.gguf	IQ3_XS	3	2.7 GB	4.7 GB	small, substantial quality loss
OpenCodeInterpreter-DS-6.7B.IQ3_S.gguf	IQ3_S	3	2.8 GB	4.8 GB	small, greater quality loss
OpenCodeInterpreter-DS-6.7B.IQ3_M.gguf	IQ3_M	3	3.0 GB	5.0 GB	medium, balanced quality - recommended
OpenCodeInterpreter-DS-6.7B.IQ4_XS.gguf	IQ4_XS	4	3.4 GB	5.4 GB	small, substantial quality loss

Generated importance matrix file: 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.

Example llama.cpp command

Make sure you are using llama.cpp from commit 0becb22 or later.

./main -ngl 33 -m OpenCodeInterpreter-DS-6.7B.IQ3_M.gguf --color -c 16384 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer.\n### Instruction:\n{prompt}\n### Response:"

Change -ngl 33 to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.

Change -c 16384 to the desired sequence length.

If you want to have a chat-style conversation, replace the -p <PROMPT> argument with -i -ins

If you are low on V/RAM try quantizing the K-cache with -ctk q8_0 or even -ctk q4_0 for big memory savings (depending on context size). There is a similar option for V-cache (-ctv), however that is not working yet.

For other parameters and how to use them, please refer to the llama.cpp documentation

How to run from Python code

You can use GGUF models from Python using the llama-cpp-python module.

How to load this model in Python code, using llama-cpp-python

For full documentation, please see: llama-cpp-python docs.

First install the package

Run one of the following commands, according to your system:

# Prebuilt wheel with basic CPU support
pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cpu
# Prebuilt wheel with NVidia CUDA acceleration
pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu121 (or cu122 etc.)
# Prebuilt wheel with Metal GPU acceleration
pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/metal
# Build base version with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUDA=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# Or with Vulkan acceleration
CMAKE_ARGS="-DLLAMA_VULKAN=on" pip install llama-cpp-python
# Or with Kompute acceleration
CMAKE_ARGS="-DLLAMA_KOMPUTE=on" pip install llama-cpp-python
# Or with SYCL acceleration
CMAKE_ARGS="-DLLAMA_SYCL=on -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx" pip install llama-cpp-python

# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_CUDA=on"
pip install llama-cpp-python

Simple llama-cpp-python example code

from llama_cpp import Llama

# Chat Completion API

llm = Llama(model_path="./OpenCodeInterpreter-DS-6.7B.IQ3_M.gguf", n_gpu_layers=33, n_ctx=16384)
print(llm.create_chat_completion(
    messages = [
        {"role": "system", "content": "You are an expert AI coding assistant."},
        {
            "role": "user",
            "content": "Pick a LeetCode challenge and solve it in Python."
        }
    ]
))

Original model card: Multimodal Art Projection's OpenCodeInterpreter DS 6.7B

OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement

[🏠Homepage] | [🛠️Code]

---

Introduction

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.

For further information and related work, refer to our paper: "OpenCodeInterpreter: A System for Enhanced Code Generation and Execution" available on arXiv.

Model Information

This model is based on deepseek-coder-6.7b-base.

Model Usage

Inference

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_path="m-a-p/OpenCodeInterpreter-DS-6.7B"

tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
model.eval()

prompt = "Write a function to find the shared elements from the given two lists."
inputs = tokenizer.apply_chat_template(
        [{'role': 'user', 'content': prompt }],
        return_tensors="pt"
    ).to(model.device)
outputs = model.generate(
    inputs, 
    max_new_tokens=1024,
    do_sample=False,
    pad_token_id=tokenizer.eos_token_id,
    eos_token_id=tokenizer.eos_token_id,
)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))

Contact

If you have any inquiries, please feel free to raise an issue or reach out to us via email at: [email protected], [email protected]. We're here to assist you!"