RichardErkhov/spdev0_-_SmolLM2-FT-MyDatasetv1-gguf

Quantization made by Richard Erkhov.

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SmolLM2-FT-MyDatasetv1 - GGUF

• Model creator: https://huggingface.co/spdev0/
• Original model: https://huggingface.co/spdev0/SmolLM2-FT-MyDatasetv1/

Name	Quant method	Size
SmolLM2-FT-MyDatasetv1.Q2_K.gguf	Q2_K	0.08GB
SmolLM2-FT-MyDatasetv1.IQ3_XS.gguf	IQ3_XS	0.08GB
SmolLM2-FT-MyDatasetv1.IQ3_S.gguf	IQ3_S	0.08GB
SmolLM2-FT-MyDatasetv1.Q3_K_S.gguf	Q3_K_S	0.08GB
SmolLM2-FT-MyDatasetv1.IQ3_M.gguf	IQ3_M	0.08GB
SmolLM2-FT-MyDatasetv1.Q3_K.gguf	Q3_K	0.09GB
SmolLM2-FT-MyDatasetv1.Q3_K_M.gguf	Q3_K_M	0.09GB
SmolLM2-FT-MyDatasetv1.Q3_K_L.gguf	Q3_K_L	0.09GB
SmolLM2-FT-MyDatasetv1.IQ4_XS.gguf	IQ4_XS	0.09GB
SmolLM2-FT-MyDatasetv1.Q4_0.gguf	Q4_0	0.09GB
SmolLM2-FT-MyDatasetv1.IQ4_NL.gguf	IQ4_NL	0.09GB
SmolLM2-FT-MyDatasetv1.Q4_K_S.gguf	Q4_K_S	0.1GB
SmolLM2-FT-MyDatasetv1.Q4_K.gguf	Q4_K	0.1GB
SmolLM2-FT-MyDatasetv1.Q4_K_M.gguf	Q4_K_M	0.1GB
SmolLM2-FT-MyDatasetv1.Q4_1.gguf	Q4_1	0.09GB
SmolLM2-FT-MyDatasetv1.Q5_0.gguf	Q5_0	0.1GB
SmolLM2-FT-MyDatasetv1.Q5_K_S.gguf	Q5_K_S	0.1GB
SmolLM2-FT-MyDatasetv1.Q5_K.gguf	Q5_K	0.1GB
SmolLM2-FT-MyDatasetv1.Q5_K_M.gguf	Q5_K_M	0.1GB
SmolLM2-FT-MyDatasetv1.Q5_1.gguf	Q5_1	0.1GB
SmolLM2-FT-MyDatasetv1.Q6_K.gguf	Q6_K	0.13GB
SmolLM2-FT-MyDatasetv1.Q8_0.gguf	Q8_0	0.13GB

Original model description:

base_model: HuggingFaceTB/SmolLM2-135M library_name: transformers model_name: SmolLM2-FT-MyDatasetv1 tags: - generated_from_trainer - smol-course - module_1 - trl - sft licence: license

Model Card for SmolLM2-FT-MyDatasetv1

This model is a fine-tuned version of HuggingFaceTB/SmolLM2-135M. It has been trained using TRL.

Quick start

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="cslm/SmolLM2-FT-MyDatasetv1", 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: 4.47.1
• Pytorch: 2.5.1+cu121
• Datasets: 3.2.0
• Tokenizers: 0.21.0

Citations

Cite TRL as:

@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}}
}