Downloads · 30 days
94
7% of all-time downloads
penny5-dolphin-einstein-llama3-dare-ties-chatml.gguf
giannisan/penny5-dolphin-einstein-llama3-dare-ties-chatml.gguf
penny5-dolphin-einstein-llama3-dare-ties-chatml.gguf is a machine learning model from giannisan. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as llama3.
All-time downloads
1.4K
Public
Repo size
19.1 GB
Likes
1
Public
Repo makeup
Click a slice to open those files.
.gguf19.1 GB · 100%
At a glance
- Library
- transformers
- License
- llama3
- Access
- Public
- Created
- May 21, 2024
- Updated
- May 31, 2024
- SHA
- ca16a5a2
Model card
From the Hugging Face model README
Model Card for Model ID
This is a fine tune of a merged model using the DARE TIES merge method using cognitivecomputations/dolphin-2.9-llama3-8b as a base. The following models were included in the merge:
Model Details
Quant Q8_0 GGUF
Model Description
<!-- Provide a longer summary of what this model is. -->This is the model card of a 🤗 transformers model that has been pushed on the Hub.
- Developed by: Gianni Sanrochman
- Funded by: [Merildo Sanrochman]
- Model type: LLaMA-3
- Language(s) (NLP): [English]
- License: llama3
- Finetuned from model: giannisan/dolphin-einstein-llama3-dare-ties using the PENNY dataset
Training Details
Training Data
<!-- 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. -->[More Information Needed]
Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->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]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->[More Information Needed]
Evaluation
| Metric | Value |
|---|---|
| Avg. | 66.72 |
| ARC (25-shot) | 61.01 |
| HellaSwag (10-shot) | 82.50 |
| MMLU (5-shot) | 64.48 |
| TruthfulQA (0-shot) | 50.73 |
| Winogrande (5-shot) | 74.11 |
| GSM8K (5-shot) | 67.48 |
full results here
Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: [Nvidia RTX A100]
- Hours used: [2]
- Cloud Provider: [RunPod]
- Compute Region: [Europe]
- Carbon Emitted: [More Information Needed]
Model Card Authors [optional]
[Gianni Sanrochman]
Model Card Contact
[More Information Needed]
- Library
- transformers
- License
- llama3
- Languages
- en
- Created
- May 21, 2024
- Updated
- May 31, 2024