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RichardErkhov/
m-a-p_-_OpenCodeInterpreter-DS-6.7B-gguf

Name: m-a-p_-_OpenCodeInterpreter-DS-6.7B-gguf
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OpenCodeInterpreter-DS-6.7B - GGUF

Model creator: https://huggingface.co/m-a-p/
Original model: https://huggingface.co/m-a-p/OpenCodeInterpreter-DS-6.7B/

Name	Quant method	Size
OpenCodeInterpreter-DS-6.7B.Q2_K.gguf	Q2_K	2.36GB
OpenCodeInterpreter-DS-6.7B.IQ3_XS.gguf	IQ3_XS	2.61GB
OpenCodeInterpreter-DS-6.7B.IQ3_S.gguf	IQ3_S	2.75GB
OpenCodeInterpreter-DS-6.7B.Q3_K_S.gguf	Q3_K_S	2.75GB
OpenCodeInterpreter-DS-6.7B.IQ3_M.gguf	IQ3_M	2.9GB
OpenCodeInterpreter-DS-6.7B.Q3_K.gguf	Q3_K	3.07GB
OpenCodeInterpreter-DS-6.7B.Q3_K_M.gguf	Q3_K_M	3.07GB
OpenCodeInterpreter-DS-6.7B.Q3_K_L.gguf	Q3_K_L	3.35GB
OpenCodeInterpreter-DS-6.7B.IQ4_XS.gguf	IQ4_XS	3.4GB
OpenCodeInterpreter-DS-6.7B.Q4_0.gguf	Q4_0	3.56GB
OpenCodeInterpreter-DS-6.7B.IQ4_NL.gguf	IQ4_NL	3.59GB
OpenCodeInterpreter-DS-6.7B.Q4_K_S.gguf	Q4_K_S	3.59GB
OpenCodeInterpreter-DS-6.7B.Q4_K.gguf	Q4_K	3.8GB
OpenCodeInterpreter-DS-6.7B.Q4_K_M.gguf	Q4_K_M	3.8GB
OpenCodeInterpreter-DS-6.7B.Q4_1.gguf	Q4_1	3.95GB
OpenCodeInterpreter-DS-6.7B.Q5_0.gguf	Q5_0	4.33GB
OpenCodeInterpreter-DS-6.7B.Q5_K_S.gguf	Q5_K_S	4.33GB
OpenCodeInterpreter-DS-6.7B.Q5_K.gguf	Q5_K	4.46GB
OpenCodeInterpreter-DS-6.7B.Q5_K_M.gguf	Q5_K_M	4.46GB
OpenCodeInterpreter-DS-6.7B.Q5_1.gguf	Q5_1	4.72GB
OpenCodeInterpreter-DS-6.7B.Q6_K.gguf	Q6_K	5.15GB

Original model description:

language:

en pipeline_tag: text-generation tags:
code license: apache-2.0

OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement

OpenCodeInterpreter

[🏠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.

Benchmark Scores

The OpenCodeInterpreter Models series exemplifies the evolution of coding model performance, particularly highlighting the significant enhancements brought about by the integration of execution feedback. In an effort to quantify these improvements, we present a detailed comparison across two critical benchmarks: HumanEval and MBPP. This comparison not only showcases the individual performance metrics on each benchmark but also provides an aggregated view of the overall performance enhancement. The subsequent table succinctly encapsulates the performance data, offering a clear perspective on how execution feedback contributes to elevating the models' capabilities in code interpretation and execution tasks.

Benchmark	HumanEval (+)	MBPP (+)	Average (+)
OpenCodeInterpreter-DS-1.3B	65.2 (61.0)	63.4 (52.4)	64.3 (56.7)
+ Execution Feedback	65.2 (62.2)	65.2 (55.6)	65.2 (58.9)
OpenCodeInterpreter-DS-6.7B	76.2 (72.0)	73.9 (63.7)	75.1 (67.9)
+ Execution Feedback	81.1 (78.7)	82.7 (72.4)	81.9 (75.6)
+ Synth. Human Feedback	87.2 (86.6)	86.2 (74.2)	86.7 (80.4)
+ Synth. Human Feedback (Oracle)	89.7 (86.6)	87.2 (75.2)	88.5 (80.9)
OpenCodeInterpreter-DS-33B	79.3 (74.3)	78.7 (66.4)	79.0 (70.4)
+ Execution Feedback	82.9 (80.5)	83.5 (72.2)	83.2 (76.4)
+ Synth. Human Feedback	88.4 (86.0)	87.5 (75.9)	88.0 (81.0)
+ Synth. Human Feedback (Oracle)	92.7 (89.7)	90.5 (79.5)	91.6 (84.6)
OpenCodeInterpreter-CL-7B	72.6 (67.7)	66.4 (55.4)	69.5 (61.6)
+ Execution Feedback	75.6 (70.1)	69.9 (60.7)	72.8 (65.4)
OpenCodeInterpreter-CL-13B	77.4 (73.8)	70.7 (59.2)	74.1 (66.5)
+ Execution Feedback	81.1 (76.8)	78.2 (67.2)	79.7 (72.0)
OpenCodeInterpreter-CL-34B	78.0 (72.6)	73.4 (61.4)	75.7 (67.0)
+ Execution Feedback	81.7 (78.7)	80.2 (67.9)	81.0 (73.3)
OpenCodeInterpreter-CL-70B	76.2 (70.7)	73.0 (61.9)	74.6 (66.3)
+ Execution Feedback	79.9 (77.4)	81.5 (69.9)	80.7 (73.7)
OpenCodeInterpreter-GM-7B	56.1 (50.0)	39.8 (34.6)	48.0 (42.3)
+ Execution Feedback	64.0 (54.3)	48.6 (40.9)	56.3 (47.6)
OpenCodeInterpreter-SC2-3B	65.2 (57.9)	62.7 (52.9)	64.0 (55.4)
+ Execution Feedback	67.1 (60.4)	63.4 (54.9)	65.3 (57.7)
OpenCodeInterpreter-SC2-7B	73.8 (68.9)	61.7 (51.1)	67.8 (60.0)
+ Execution Feedback	75.6 (69.5)	66.9 (55.4)	71.3 (62.5)
OpenCodeInterpreter-SC2-15B	75.6 (69.5)	71.2 (61.2)	73.4 (65.4)
+ Execution Feedback	77.4 (72.0)	74.2 (63.4)	75.8 (67.7)

Note: The "(+)" notation represents scores from extended versions of the HumanEval and MBPP benchmarks. To ensure a fair comparison, the results shown for adding execution feedback are based on outcomes after just one iteration of feedback, without unrestricted iterations. This approach highlights the immediate impact of execution feedback on performance improvements across benchmarks.

Model Usage

Inference

python

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: xiangyue.work@gmail.com, zhengtianyu0428@gmail.com. We're here to assist you!"

RichardErkhov/m-a-p_-_OpenCodeInterpreter-DS-6.7B-gguf