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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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<!-- Provide a longer summary of what this model is. -->
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- **Developed by:**
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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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- **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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<!-- 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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<!-- 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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[More Information Needed]
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license: apache-2.0
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language:
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- en
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pipeline_tag: text-generation
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# llama-3-neural-chat-v2.2-8b
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<!-- Provide a quick summary of what the model is/does. -->
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![image/png](https://cdn-uploads.huggingface.co/production/uploads/6437292ecd93f4c9a34b0d47/6XQuhjWNr6C4RbU9f1k99.png)
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## Model Details
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<!-- Provide a longer summary of what this model is. -->
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I fine-tuned llama-3 8B on an approach similar to Intel's neural chat language model. I have slightly modified the data sources so it is stronger in coding, math, and writing. I use both SFT and DPO.
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- **Developed by:** Locutusque
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- **Model type:** Built with Meta Llama 3
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- **Language(s) (NLP):** Many?
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- **License:** Llama 3 license https://huggingface.co/meta-llama/Meta-Llama-3-8B/blob/main/LICENSE
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## Quants
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coming soon
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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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This model has great performance in writing, coding, and math.
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## Training Data
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Recipe information will be coming soon. This language model's recipe is similar to Intel's Neural Chat.
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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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Conversational AI.
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## Evaluations
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| Tasks |Version| Filter |n-shot| Metric |Value | |Stderr|
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|---------------------------------|-------|----------------|-----:|-----------|-----:|---|-----:|
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|truthfulqa_mc2 | 2|none | 0|acc |0.5232|± |0.0151|
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|gsm8k | 3|strict-match | 5|exact_match|0.5974|± |0.0135|
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| | |flexible-extract| 5|exact_match|0.5974|± |0.0135|
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|agieval_nous |N/A |none | 0|acc_norm |0.3841|± |0.0094|
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| | |none | 0|acc |0.3802|± |0.0094|
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| - agieval_aqua_rat | 1|none | 0|acc |0.2598|± |0.0276|
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| | |none | 0|acc_norm |0.2520|± |0.0273|
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| - agieval_logiqa_en | 1|none | 0|acc |0.3441|± |0.0186|
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| | |none | 0|acc_norm |0.3687|± |0.0189|
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| - agieval_lsat_ar | 1|none | 0|acc |0.2217|± |0.0275|
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| | |none | 0|acc_norm |0.2348|± |0.0280|
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| - agieval_lsat_lr | 1|none | 0|acc |0.3882|± |0.0216|
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| | |none | 0|acc_norm |0.3824|± |0.0215|
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| - agieval_lsat_rc | 1|none | 0|acc |0.4944|± |0.0305|
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| | |none | 0|acc_norm |0.5019|± |0.0305|
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| - agieval_sat_en | 1|none | 0|acc |0.6650|± |0.0330|
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| | |none | 0|acc_norm |0.6553|± |0.0332|
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| - agieval_sat_en_without_passage| 1|none | 0|acc |0.3981|± |0.0342|
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| | |none | 0|acc_norm |0.3981|± |0.0342|
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| - agieval_sat_math | 1|none | 0|acc |0.3500|± |0.0322|
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| | |none | 0|acc_norm |0.3318|± |0.0318|
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