Transformers
Safetensors
Generated from Trainer
sft
trl
hf_jobs
ml-intern
deepwiki
code-documentation
technical-writing
Instructions to use GhostScientist/semanticwiki-coder-7b-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GhostScientist/semanticwiki-coder-7b-v2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("GhostScientist/semanticwiki-coder-7b-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
End of training
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README.md
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licence: license
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---
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url = {https://github.com/huggingface/trl},
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year = {2020}
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```
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<!-- ml-intern-provenance -->
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## Generated by ML Intern
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This model repository was generated by [ML Intern](https://github.com/huggingface/ml-intern), an agent for machine learning research and development on the Hugging Face Hub.
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- Try ML Intern: https://smolagents-ml-intern.hf.space
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- Source code: https://github.com/huggingface/ml-intern
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = 'GhostScientist/deepwiki-coder-7b-v2'
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id)
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```
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For non-causal architectures, replace `AutoModelForCausalLM` with the appropriate `AutoModel` class.
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licence: license
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---
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url = {https://github.com/huggingface/trl},
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year = {2020}
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}
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```
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