You can deploy this model and start earning money today!

ShethArihant/
deepseek-coder-1.3b-instruct_sft-v4_3-epochs_ce-0.8_triplet-0.2_last-token

Name: deepseek-coder-1.3b-instruct_sft-v4_3-epochs_ce-0.8_triplet-0.2_last-token
Availability: InStock

generated_from_trainer

sft

trl

conversational

deepseek-ai/deepseek-coder-1.3b-instruct

finetune

text-generation-inference

endpoints_compatible

Views

No views yet

Model Card for deepseek-coder-1.3b-instruct_sft-v4_3-epochs_ce-0.8_triplet-0.2_last-token

This model is a fine-tuned version of deepseek-ai/deepseek-coder-1.3b-instruct. 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="ShethArihant/deepseek-coder-1.3b-instruct_sft-v4_3-epochs_ce-0.8_triplet-0.2_last-token", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

Visualize in Weights & Biases

This model was trained with SFT.

Framework versions

TRL: 0.24.0
Transformers: 4.57.1
Pytorch: 2.8.0+cu126
Datasets: 4.0.0
Tokenizers: 0.22.1

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 Gallou{\'e}dec},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
}