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README.md
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pipeline_tag: text-generation
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---
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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## Model Card Contact
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[More Information Needed]
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pipeline_tag: text-generation
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---
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Converted version of [Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) to 4-bit using bitsandbytes. For more information about the model,
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refer to the model's page.
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## Impact on performance
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Impact of quantization on a set of models.
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Evaluation of the model was conducted using the **PoLL (Pool of LLM)** technique, focusing on **100 French programming and computer science questions** spanning
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topics in pseudo-code, Java, Python, and JavaScript. Performance was assessed through scores aggregated from **six evaluations** (two per evaluator). The evaluators
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included GPT-4o, Gemini-1.5-pro, and Claude3.5-sonnet, each analyzing the model’s understanding and response accuracy on code syntax, logic, and technical
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explanations across these languages and formats.
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Performance Scores (on a scale of 5):
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| Model | Score | # params (Billion) | size (GB) |
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|:----------------------------------------------------------|:--------:|:------------------:|:---------:|
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| ('Qwen/Qwen2.5-Coder-1.5B', 'Java') | 0.545072 |\n| ('Qwen/Qwen2.5-Coder-1.5B', 'JavaScript') | 0.436294 |\n| ('Qwen/Qwen2.5-Coder-1.5B', 'Python') | 0.434845 |\n| ('bigcode/starcoder2-15b', 'Java') | 0.62873 |\n| ('bigcode/starcoder2-15b', 'JavaScript') | 0.536091 |\n| ('bigcode/starcoder2-15b', 'Python') | 0.510945 |\n| ('bigcode/starcoder2-3b', 'Java') | 0.595437 |\n| ('bigcode/starcoder2-3b', 'JavaScript') | 0.500828 |\n| ('bigcode/starcoder2-3b', 'Python') | 0.479801 |\n| ('bigcode/starcoder2-7b', 'Java') | 0.574745 |\n| ('bigcode/starcoder2-7b', 'JavaScript') | 0.465017 |\n| ('bigcode/starcoder2-7b', 'Python') | 0.453512 |\n| ('cmarkea/CodeLlama-34b-hf-4bit', 'Java') | 0.724881 |\n| ('cmarkea/CodeLlama-34b-hf-4bit', 'JavaScript') | 0.609453 |\n| ('cmarkea/CodeLlama-34b-hf-4bit', 'Python') | 0.596516 |\n| ('cmarkea/CodeLlama-70b-hf-4bit', 'Java') | 0.760241 |\n| ('cmarkea/CodeLlama-70b-hf-4bit', 'JavaScript') | 0.652255 |\n| ('cmarkea/CodeLlama-70b-hf-4bit', 'Python') | 0.640797 |\n| ('cmarkea/CodeLlama-7b-hf-4bit', 'Java') | 0.66728 |\n| ('cmarkea/CodeLlama-7b-hf-4bit', 'JavaScript') | 0.542181 |\n| ('cmarkea/CodeLlama-7b-hf-4bit', 'Python') | 0.529498 |\n| ('deepseek-ai/deepseek-coder-1.3b-base', 'Java') | 0.586311 |\n| ('deepseek-ai/deepseek-coder-1.3b-base', 'JavaScript') | 0.450518 |\n| ('deepseek-ai/deepseek-coder-1.3b-base', 'Python') | 0.487962 |\n| ('deepseek-ai/deepseek-coder-6.7b-base', 'Java') | 0.6271 |\n| ('deepseek-ai/deepseek-coder-6.7b-base', 'JavaScript') | 0.496296 |\n| ('deepseek-ai/deepseek-coder-6.7b-base', 'Python') | 0.532503 |\n| ('deepseek-ai/deepseek-coder-7b-base-v1.5', 'Java') | 0.623582 |\n| ('deepseek-ai/deepseek-coder-7b-base-v1.5', 'JavaScript') | 0.503935 |\n| ('deepseek-ai/deepseek-coder-7b-base-v1.5', 'Python') | 0.521639 |\n| ('meta-llama/CodeLlama-13b-hf', 'Java') | 0.680939 |\n| ('meta-llama/CodeLlama-13b-hf', 'JavaScript') | 0.563145 |\n| ('meta-llama/CodeLlama-13b-hf', 'Python') | 0.546593 |\n| ('meta-llama/CodeLlama-34b-hf', 'Java') | 0.728144 |\n| ('meta-llama/CodeLlama-34b-hf', 'JavaScript') | 0.613087 |\n| ('meta-llama/CodeLlama-34b-hf', 'Python') | 0.599918 |\n| ('meta-llama/CodeLlama-70b-hf', 'Java') | 0.76577 |\n| ('meta-llama/CodeLlama-70b-hf', 'JavaScript') | 0.658665 |\n| ('meta-llama/CodeLlama-70b-hf', 'Python') | 0.647235 |\n| ('meta-llama/CodeLlama-7b-hf', 'Java') | 0.670398 |\n| ('meta-llama/CodeLlama-7b-hf', 'JavaScript') | 0.546067 |\n| ('meta-llama/CodeLlama-7b-hf', 'Python') | 0.533356 |\n| ('mistralai/Codestral-22B-v0.1', 'Java') | 0.631256 |\n| ('mistralai/Codestral-22B-v0.1', 'JavaScript') | 0.553193 |\n| ('mistralai/Codestral-22B-v0.1', 'Python') | 0.544975 |
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| Model | Score | # params (Billion) | size (GB) |
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|---------------------------------------------:|:--------:|:------------------:|:---------:|
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| gpt-4o | 4.51 | N/A | N/A |
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| deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct | 4.24 | 15.7 | 31.4 |
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| meta-llama/Meta-Llama-3.1-70B-Instruct | 4.23 | 70.06 | 141.2 |
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| cmarkea/Meta-Llama-3.1-70B-Instruct-4bit | 4.14 | 70.06 | 35.3 |
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| Qwen/Qwen2.5-Coder-7B-Instruct | 4.11 | 7.62 | 15.24 |
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| **cmarkea/Qwen2.5-Coder-7B-Instruct-4bit** | **4.08** | **7.62** | **3.81** |
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| cmarkea/Mixtral-8x7B-Instruct-v0.1-4bit | 3.8 | 46.7 | 23.35 |
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| meta-llama/Meta-Llama-3.1-8B-Instruct | 3.73 | 8.03 | 16.06 |
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| mistralai/Mixtral-8x7B-Instruct-v0.1 | 3.33 | 46.7 | 93.4 |
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| codellama/CodeLlama-13b-Instruct-hf | 3.33 | 13 | 26 |
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| codellama/CodeLlama-34b-Instruct-hf | 3.27 | 33.7 | 67.4 |
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| codellama/CodeLlama-7b-Instruct-hf | 3.19 | 6.74 | 13.48 |
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| cmarkea/CodeLlama-34b-Instruct-hf-4bit | 3.12 | 33.7 | 16.35 |
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| codellama/CodeLlama-70b-Instruct-hf | 1.82 | 69 | 138 |
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| cmarkea/CodeLlama-70b-Instruct-hf-4bit | 1.64 | 69 | 34.5 |
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The impact of quantization is negligible.
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## Prompt Pattern
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Here is a reminder of the command pattern to interact with the model:
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```verbatim
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<|im_start|>user\n{user_prompt_1}<|im_end|><|im_start|>assistant\n{model_answer_1}<|im_end|>...
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```
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