from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda"
bolmo = AutoModelForCausalLM.from_pretrained("AlekseyCalvin/Lyrical_Bolmo_7b_SFT_Merged", trust_remote_code=True).to(device)
tokenizer = AutoTokenizer.from_pretrained("AlekseyCalvin/Lyrical_Bolmo_7b_SFT_Merged", trust_remote_code=True)
message = ["Translate the following verses: Совершить ли мне горшочек для вмещения кишочек вымещения червей красоты земли моей  "]
input_ids = tokenizer(message, return_tensors="pt")["input_ids"].to(device)
# `max_new_tokens` is the amount of bytes to generate
response = bolmo.generate(input_ids, max_new_tokens=256, do_sample=True, temperature=0.1)
print(tokenizer.decode(response[0], skip_special_tokens=True))

Training procedure

Visualize in Weights & Biases

This model was trained with SFT.

Framework versions

TRL: 0.26.2
Sacrebleu (for BLEU Machine Translation evaluations)
Transformers: 4.57.3
Pytorch: 2.9.0+cu126
Datasets: 4.0.0
Tokenizers: 0.22.1

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}}
}