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---
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### Model Description
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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###
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[More Information Needed]
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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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#### Software
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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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language:
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- en
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license: other
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tags:
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- chat
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license_name: tongyi-qianwen
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license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE
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pipeline_tag: text-generation
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# Dracarys2-72B-Instruct
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# Introduction
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We introduce the latest in the Smaug series, the Dracarys family of finetunes targeting coding performance improvements
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across a variety of base models.
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This variant is a finetune of [Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct)
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Compared to Qwen2.5-72B-Instruct, Dracarys has better LiveCodeBench scores (see evaluation results below).
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### Model Description
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- **Developed by:** [Abacus.AI](https://abacus.ai)
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- **License:** https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE
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- **Finetuned from model:** [Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct).
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## How to use
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The prompt format is unchanged from Qwen2.5-72B-Instruct (see evaluations for prompt details for LCB)
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### Use with transformers
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See the snippet below for usage with Transformers:
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```python
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import transformers
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import torch
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model_id = "abacusai/Dracarys2-72B-Instruct"
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pipeline = transformers.pipeline(
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"text-generation",
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model=model_id,
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model_kwargs={"torch_dtype": torch.bfloat16},
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device_map="auto",
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)
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messages = [
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{"role": "system", "content": "You are data science coding assistant that generates Python code using Pandas and Numpy."},
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{"role": "user", "content": "Write code to select rows from the dataframe `df` having the maximum `temp` for each `city`"},
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]
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prompt = pipeline.tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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terminators = [
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pipeline.tokenizer.eos_token_id,
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pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
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]
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outputs = pipeline(
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prompt,
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max_new_tokens=256,
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eos_token_id=terminators,
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do_sample=True,
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temperature=0.6,
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top_p=0.9,
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print(outputs[0]["generated_text"][len(prompt):])
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```
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# Evaluation Results
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## LiveCodeBench
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| Model | Code Generation | Code Execution (COT) |Test Output Prediction |
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|----------------------------|-----------------|----------------------|-----------------------|
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| **Dracarys2-72B-Instruct** | **53.80** | **89.12** | **59.61** |
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| Qwen2.5-72B-Instruct | 53.03 | 88.72 | 46.28 |
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## Breakdown of LiveCodeBench CodeGeneration
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| Model | Easy | Medium | Hard |
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|---------------------------|-----------------|----------------|---------------|
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| **Dracarys2-72B-Instruct**| **88.79** | **50.28** | 9.47 |
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| Qwen2.5-72B-Instruct | 86.99 | 49.59 | 9.99 |
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## Breakdown of LiveCodeBench TestOutputPrediction
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| Model | Easy | Medium | Hard |
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|---------------------------|-----------------|----------------|-----------------------|
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| **Dracarys2-72B-Instruct**| **79.25** | **53.76** | **37.63** |
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| Qwen2.5-72B-Instruct | 68.43 | 39.46 | 22.22 |
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