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
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
- Developed, Funded, and Shared by: Jacob Leuthardt
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- Language(s) (NLP): [More Information Needed]
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Model Sources [optional]
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Uses
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
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
Preprocessing [optional]
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Training Hyperparameters
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Speeds, Sizes, Times [optional]
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Evaluation
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
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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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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APA:
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Glossary [optional]
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Model Card Authors [optional]
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Model Card Contact
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