from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("tendrivalentin/witcher3-qwen35-08b-sft-ptbr")
tokenizer = AutoTokenizer.from_pretrained("tendrivalentin/witcher3-qwen35-08b-sft-ptbr")

messages = [{"role": "user", "content": "Quem é Ciri e qual é a sua importância?"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=150, temperature=0.7, do_sample=True)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

From Local Path

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "runs/sft-witcher3-ptbr-qwen35-08b-full"
model = AutoModelForCausalLM.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)

messages = [{"role": "user", "content": "Fale sobre Yennefer."}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=150, temperature=0.7, do_sample=True)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Results

Metric	Value
Train Loss	0.660
Train Accuracy	85.07%
Eval Loss (best)	2.391
Eval Accuracy	53.93%
Training Time	~35 min
Hardware	Apple Silicon (MPS)

Training Procedure

This model was trained with SFT.

Framework versions

TRL: 1.7.0
Transformers: 5.13.0
Pytorch: 2.12.1
Datasets: 5.0.0
Tokenizers: 0.22.2

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}
}