Feature Extraction
Transformers
Safetensors
mistral
Merge
mergekit
lazymergekit
Syed-Hasan-8503/Tess-Coder-7B-Mistral-v1.0
mlabonne/AlphaMonarch-7B
Eval Results (legacy)
text-embeddings-inference
Instructions to use QueryloopAI/MonarchCoder-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QueryloopAI/MonarchCoder-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="QueryloopAI/MonarchCoder-7B")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("QueryloopAI/MonarchCoder-7B") model = AutoModel.from_pretrained("QueryloopAI/MonarchCoder-7B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model-00004-of-00006.safetensors from QueryloopAI/MonarchCoder-7B: direct link, hf CLI and curl.
- Browser
- Download file 5 GB
-
https://huggingface.co/QueryloopAI/MonarchCoder-7B/resolve/main/model-00004-of-00006.safetensors
- Command line
-
hf download hf://QueryloopAI/MonarchCoder-7B/model-00004-of-00006.safetensors
-
curl -L -o model-00004-of-00006.safetensors https://huggingface.co/QueryloopAI/MonarchCoder-7B/resolve/main/model-00004-of-00006.safetensors
5 GB
- Xet hash:
- 308ecec814f00bf2681367f1e67bdf6da150fdde6df8979241b51ccfca2833a0
- Size of remote file:
- 5 GB
- SHA256:
- 38df4cab7aacc0177736d5ee57582a010779d7250e66958eef86ad903a104a1c
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