---
license: mit
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
library_name: transformers
tags: ["meta", "llama", "voice", "question-answering"]
---

# Model Card for Llama-Q4V

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Llama-Q4V is a quantized and voice-optimized version of Llama-3.1-8B-Instruct, and is the core LLM for Ai Tag, an open source $20 competitor to Humane's Ai Pin.

## Model Details

### Model Description

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- **Developed, Funded, and Shared by:** Jacob Leuthardt
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]

### Model Sources [optional]

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## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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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.

## How to Get Started with the Model

Use the code below to get started with the model.

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## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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Llama-Q4V was evaluated on HellaSwag, GLUE, and TriviaQA

|     Tasks      |Version|     Filter      |n-shot|  Metric   |   |Value |   |Stderr|
|----------------|-------|-----------------|-----:|-----------|---|-----:|---|-----:|
| - cola         |      1|none             |     0|mcc        |↑  |0.0409|±  |0.0323|
|glue            |N/A    |none             |     0|acc        |↑  |0.5002|±  |0.0019|
|                |       |none             |     0|f1         |↑  |0.5789|±  |0.0026|
|                |       |none             |     0|mcc        |↑  |0.0409|±  |0.0323|
|hellaswag       |      1|none             |     0|acc        |↑  |0.5849|±  |0.0049|
|                |       |none             |     0|acc_norm   |↑  |0.7846|±  |0.0041|
| - mnli         |      1|none             |     0|acc        |↑  |0.5200|±  |0.0050|
| - mnli_mismatch|      1|none             |     0|acc        |↑  |0.5098|±  |0.0050|
| - mrpc         |      1|none             |     0|acc        |↑  |0.6887|±  |0.0230|
|                |       |none             |     0|f1         |↑  |0.8113|±  |0.0165|
| - qnli         |      1|none             |     0|acc        |↑  |0.5032|±  |0.0068|
| - qqp          |      2|none             |     0|acc        |↑  |0.4816|±  |0.0025|
|                |       |none             |     0|f1         |↑  |0.5765|±  |0.0027|
| - rte          |      1|none             |     0|acc        |↑  |0.6679|±  |0.0283|
| - sst2         |      1|none             |     0|acc        |↑  |0.8612|±  |0.0117|
|triviaqa        |      3|remove_whitespace|     0|exact_match|↑  |0.4962|±  |0.0037|
| - wnli         |      2|none             |     0|acc        |↑  |0.6197|±  |0.0580|

|Groups|Version|Filter|n-shot|Metric|   |Value |   |Stderr|
|------|-------|------|-----:|------|---|-----:|---|-----:|
|glue  |N/A    |none  |     0|acc   |↑  |0.5002|±  |0.0019|
|      |       |none  |     0|f1    |↑  |0.5789|±  |0.0026|
|      |       |none  |     0|mcc   |↑  |0.0409|±  |0.0323|

### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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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).

- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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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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