README.md

metadata

base_model: openai/gpt-oss-20b
library_name: peft
license: apache-2.0
tags:
  - trl
  - sft
  - lora
  - reasoning
  - multilingual
model_type: lora

gpt-oss-20b-multilingual-reasoner

This is a LoRA (Low-Rank Adaptation) fine-tuned model based on openai/gpt-oss-20b.

Model Details

• Base Model: openai/gpt-oss-20b
• Fine-tuning Method: LoRA (Low-Rank Adaptation)
• Training Framework: TRL (Transformer Reinforcement Learning)
• LoRA Rank: 8
• LoRA Alpha: 16
• Target Modules: q_proj, o_proj, v_proj, k_proj

Usage

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