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cfan233/sst2-distilgpt2_sft_loRA_smoke_min_trackio3

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Model Card for sst2-distilgpt2sftloRAsmokemin_trackio3

This model is a fine-tuned version of distilgpt2. It has been trained using TRL.

Quick start

python

from transformers import pipeline

question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="cfan233/sst2-distilgpt2_sft_loRA_smoke_min_trackio3", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

This model was trained with SFT.

Framework versions

—TRL: 1.6.0
—Transformers: 5.12.1
—Pytorch: 2.12.0
—Datasets: 5.0.0
—Tokenizers: 0.22.1

Citations

Cite TRL as:

bibtex

@software{vonwerra2020trl,
  title   = {{TRL: Transformers Reinforcement Learning}},
  author  = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
  license = {Apache-2.0},
  url     = {https://github.com/huggingface/trl},
  year    = {2020}
}

Generated by ML Intern

This model repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.

—Try ML Intern: https://smolagents-ml-intern.hf.space
—Source code: https://github.com/huggingface/ml-intern

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = 'cfan233/sst2-distilgpt2_sft_loRA_smoke_min_trackio3'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

For non-causal architectures, replace AutoModelForCausalLM with the appropriate AutoModel class.