Instructions to use nwhamed/Merged_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nwhamed/Merged_Model with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nwhamed/Merged_Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from nwhamed/Merged_Model: direct link, hf CLI and curl.
- Browser
- Download file 717 Bytes
-
https://huggingface.co/nwhamed/Merged_Model/resolve/main/README.md
- Command line
-
hf download hf://nwhamed/Merged_Model/README.md
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curl -L -o README.md https://huggingface.co/nwhamed/Merged_Model/resolve/main/README.md
717 Bytes
| license: apache-2.0 | |
| tags: | |
| - merge | |
| - mergekit | |
| - lazymergekit | |
| - google/gemma-7b | |
| - EleutherAI/gpt-neo-2.7B | |
| # gemma_gpt | |
| gemma_gpt is a merge of the following models using [mergekit](https://github.com/cg123/mergekit): | |
| * [google/gemma-7b](https://huggingface.co/google/gemma-7b) | |
| * [EleutherAI/gpt-neo-2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B) | |
| ## 🧩 Configuration | |
| ```json{ | |
| "models": [ | |
| { | |
| "model": "google/gemma-7b", | |
| "parameters": { | |
| "param1": "value1", | |
| "param2": "value2" | |
| } | |
| }, | |
| { | |
| "model": "EleutherAI/gpt-neo-2.7B", | |
| "parameters": { | |
| "param1": "value1", | |
| "param2": "value2" | |
| } | |
| } | |
| ] | |
| } |