Instructions to use 1024m/L2-MAP-RAND with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 1024m/L2-MAP-RAND with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("1024m/L2-MAP-RAND", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
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Download README.md from 1024m/L2-MAP-RAND: direct link, hf CLI and curl.
- Browser
- Download file 578 Bytes
-
https://huggingface.co/1024m/L2-MAP-RAND/resolve/main/README.md
- Command line
-
hf download hf://1024m/L2-MAP-RAND/README.md
-
curl -L -o README.md https://huggingface.co/1024m/L2-MAP-RAND/resolve/main/README.md
578 Bytes
metadata
base_model: unsloth/phi-4-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
Uploaded model
- Developed by: 1024m
- License: apache-2.0
- Finetuned from model : unsloth/phi-4-unsloth-bnb-4bit
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
