PEFT
qlora
sft
trl
qwen3
tmf921
intent-based-networking
network-slicing
rtx-6000-ada
ml-intern
nraptisss commited on
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Update ML Intern artifact metadata

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@@ -240,3 +240,23 @@ requirements.txt
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  - TRL SFTTrainer docs: https://huggingface.co/docs/trl/sft_trainer
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  - TRL PEFT integration: https://huggingface.co/docs/trl/peft_integration
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  - Source dataset: https://huggingface.co/datasets/nraptisss/TMF921-intent-to-config-research-sota
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  - TRL SFTTrainer docs: https://huggingface.co/docs/trl/sft_trainer
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  - TRL PEFT integration: https://huggingface.co/docs/trl/peft_integration
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  - Source dataset: https://huggingface.co/datasets/nraptisss/TMF921-intent-to-config-research-sota
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+
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+ <!-- ml-intern-provenance -->
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+ ## Generated by ML Intern
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+
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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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+
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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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+
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+ ## Usage
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+
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+ ```python
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+
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+ model_id = 'nraptisss/tmf921-intent-training'
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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.