from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "openai/gpt-oss-20b",
    torch_dtype=torch.float16,
    device_map="auto"
)

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "yiwenX/gpt-oss-20b-multilingual-reasoner")

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("yiwenX/gpt-oss-20b-multilingual-reasoner")

# Generate text
inputs = tokenizer("Hello, how are you?", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Training Details

This model was fine-tuned using:

Framework: TRL (Transformer Reinforcement Learning)
Method: Supervised Fine-Tuning (SFT)
PEFT Type: LoRA
Transformers Version: 4.56.0
PyTorch Version: 2.8.0+cu128

Model Files

adapter_config.json: LoRA configuration
adapter_model.safetensors: LoRA weights
tokenizer.json: Tokenizer vocabulary
tokenizer_config.json: Tokenizer configuration
special_tokens_map.json: Special tokens mapping
chat_template.jinja: Chat template for conversation format