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  ---
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- library_name: transformers
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- tags: []
 
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  ---
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-
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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- ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- - **Developed by:** [More Information Needed]
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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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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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- ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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- ### Downstream Use [optional]
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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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- ### Results
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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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- ### Compute Infrastructure
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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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- **APA:**
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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 [optional]
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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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  ---
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+ datasets: wikitext
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+ license: other
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+ license_link: https://llama.meta.com/llama3/license/
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  ---
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+ This is a quantized model of [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) using GPTQ developed by [IST Austria](https://ist.ac.at/en/research/alistarh-group/)
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+ using the following configuration:
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+ - 4bit
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+ - Act order: True
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+ - Group size: 128
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+
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+ ## Usage
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+ Install **vLLM** and
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+ run the [server](https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html#openai-compatible-server):
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+
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+ ```
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+ python -m vllm.entrypoints.openai.api_server --model cortecs/Meta-Llama-3-8B-Instruct-GPTQ
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+ ```
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+ Access the model:
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+ ```
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+ curl http://localhost:8000/v1/completions -H "Content-Type: application/json" -d ' {
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+ "model": "cortecs/Meta-Llama-3-8B-Instruct-GPTQ",
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+ "prompt": "San Francisco is a"
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+ } '
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+ ```
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+
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+ ## Evaluations
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+ | __English__ | __[Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)__ | __[Meta-Llama-3-8B-Instruct-GPTQ-8b](https://huggingface.co/cortecs/Meta-Llama-3-8B-Instruct-GPTQ-8b)__ | __[Meta-Llama-3-8B-Instruct-GPTQ](https://huggingface.co/cortecs/Meta-Llama-3-8B-Instruct-GPTQ)__ |
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+ |:--------------|:---------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------|
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+ | Avg. | 66.97 | 67.0 | 63.52 |
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+ | ARC | 62.5 | 62.5 | 54.6 |
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+ | Hellaswag | 70.3 | 70.3 | 69.5 |
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+ | MMLU | 68.11 | 68.21 | 66.46 |
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+ | | | | |
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+ | __French__ | __[Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)__ | __[Meta-Llama-3-8B-Instruct-GPTQ-8b](https://huggingface.co/cortecs/Meta-Llama-3-8B-Instruct-GPTQ-8b)__ | __[Meta-Llama-3-8B-Instruct-GPTQ](https://huggingface.co/cortecs/Meta-Llama-3-8B-Instruct-GPTQ)__ |
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+ | Avg. | 57.73 | 57.7 | 53.33 |
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+ | Hellaswag_fr | 61.7 | 62.2 | 59.3 |
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+ | ARC_fr | 53.3 | 53.1 | 46.4 |
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+ | MMLU_fr | 58.2 | 57.8 | 54.3 |
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+ | | | | |
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+ | __German__ | __[Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)__ | __[Meta-Llama-3-8B-Instruct-GPTQ-8b](https://huggingface.co/cortecs/Meta-Llama-3-8B-Instruct-GPTQ-8b)__ | __[Meta-Llama-3-8B-Instruct-GPTQ](https://huggingface.co/cortecs/Meta-Llama-3-8B-Instruct-GPTQ)__ |
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+ | Avg. | 53.47 | 53.67 | 49.0 |
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+ | ARC_de | 49.1 | 49.0 | 41.6 |
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+ | Hellaswag_de | 55.0 | 55.2 | 53.3 |
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+ | MMLU_de | 56.3 | 56.8 | 52.1 |
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+ | | | | |
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+ | __Italian__ | __[Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)__ | __[Meta-Llama-3-8B-Instruct-GPTQ-8b](https://huggingface.co/cortecs/Meta-Llama-3-8B-Instruct-GPTQ-8b)__ | __[Meta-Llama-3-8B-Instruct-GPTQ](https://huggingface.co/cortecs/Meta-Llama-3-8B-Instruct-GPTQ)__ |
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+ | Avg. | 56.73 | 56.67 | 51.3 |
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+ | Hellaswag_it | 61.3 | 61.3 | 58.4 |
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+ | MMLU_it | 57.3 | 57.0 | 53.0 |
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+ | ARC_it | 51.6 | 51.7 | 42.5 |
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+ | | | | |
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+ | __Safety__ | __[Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)__ | __[Meta-Llama-3-8B-Instruct-GPTQ-8b](https://huggingface.co/cortecs/Meta-Llama-3-8B-Instruct-GPTQ-8b)__ | __[Meta-Llama-3-8B-Instruct-GPTQ](https://huggingface.co/cortecs/Meta-Llama-3-8B-Instruct-GPTQ)__ |
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+ | Avg. | 61.42 | 61.42 | 61.53 |
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+ | RealToxicityPrompts | 97.2 | 97.2 | 97.2 |
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+ | TruthfulQA | 51.65 | 51.58 | 51.98 |
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+ | CrowS | 35.42 | 35.48 | 35.42 |
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+ | | | | |
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+ | __Spanish__ | __[Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)__ | __[Meta-Llama-3-8B-Instruct-GPTQ-8b](https://huggingface.co/cortecs/Meta-Llama-3-8B-Instruct-GPTQ-8b)__ | __[Meta-Llama-3-8B-Instruct-GPTQ](https://huggingface.co/cortecs/Meta-Llama-3-8B-Instruct-GPTQ)__ |
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+ | Avg. | 59 | 58.63 | 54.6 |
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+ | ARC_es | 54.1 | 53.8 | 46.9 |
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+ | Hellaswag_es | 63.8 | 63.3 | 60.3 |
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+ | MMLU_es | 59.1 | 58.8 | 56.6 |
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+
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+ We did not check for data contamination.
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+ Evaluation was done using [Eval. Harness](https://github.com/EleutherAI/lm-evaluation-harness) using `limit=1000`.
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+
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+ ## Performance
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+ | | requests/s | tokens/s |
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+ |:------------|-------------:|-----------:|
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+ | NVIDIA L4x1 | 3.96 | 1887.55 |
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+ | NVIDIA L4x2 | 4.87 | 2323.34 |
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+ | NVIDIA L4x4 | 5.61 | 2674.18 |
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+ Performance measured on [cortecs inference](https://cortecs.ai).