Model save

README.md

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+---
+base_model: HuggingFaceTB/SmolVLM-Instruct
+library_name: transformers
+model_name: SmolVLM-Instruct-SFT
+tags:
+- generated_from_trainer
+- unsloth
+- trl
+- sft
+licence: license
+---
+
+# Model Card for SmolVLM-Instruct-SFT
+
+This model is a fine-tuned version of [HuggingFaceTB/SmolVLM-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM-Instruct).
+It has been trained using [TRL](https://github.com/huggingface/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="mjschock/SmolVLM-Instruct-SFT", 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: 0.13.0
+- Transformers: 4.48.0.dev0
+- Pytorch: 2.5.1
+- Datasets: 3.2.0
+- Tokenizers: 0.21.0
+
+## Citations
+
+
+
+Cite TRL as:
+    
+```bibtex
+@misc{vonwerra2022trl,
+	title        = {{TRL: Transformer Reinforcement Learning}},
+	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
+	year         = 2020,
+	journal      = {GitHub repository},
+	publisher    = {GitHub},
+	howpublished = {\url{https://github.com/huggingface/trl}}
+}
+```

train_dataset_state_dict.json

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+{"ex_iterable_idx": 0, "ex_iterables": [{"ex_iterable": {"shard_idx": 0, "shard_example_idx": 0}, "previous_state": null, "num_examples_since_previous_state": 0, "previous_state_example_idx": 0}, {"ex_iterable": {"shard_idx": 0, "shard_example_idx": 0}, "previous_state": null, "num_examples_since_previous_state": 0, "previous_state_example_idx": 0}, {"ex_iterable": {"shard_idx": 0, "shard_example_idx": 0}, "previous_state": null, "num_examples_since_previous_state": 0, "previous_state_example_idx": 0}], "previous_states": [null, null, null], "is_exhausted": [false, false, false]}