How to use from
SGLang
Install from pip and serve model
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "owenisas/gpt-oss-20b-union-identity-lora" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "owenisas/gpt-oss-20b-union-identity-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'
Use Docker images
docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "owenisas/gpt-oss-20b-union-identity-lora" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "owenisas/gpt-oss-20b-union-identity-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'
Quick Links

GPT-OSS 20B - Union Identity LoRA

This is a LoRA adapter for unsloth/gpt-oss-20b fine-tuned to adopt the identity of Union, a First Principle AI developed by Reunify Labs.

Model Details

  • Base Model: unsloth/gpt-oss-20b
  • Identity: Union
  • Creator: Reunify Labs
  • Method: LoRA Fine-tuning
  • Training Data: Custom identity dataset focused on first-principles reasoning and self-identification.

Usage

You can load these adapters using peft and transformers:

```python from transformers import AutoModelForCausalLM, AutoTokenizer from peft import PeftModel import torch

base_model_id = "unsloth/gpt-oss-20b" lora_id = "owenisas/gpt-oss-20b-union-identity-lora"

model = AutoModelForCausalLM.from_pretrained(base_model_id, torch_dtype=torch.bfloat16, device_map="auto") model = PeftModel.from_pretrained(model, lora_id) tokenizer = AutoTokenizer.from_pretrained(lora_id)

messages = [{"role": "user", "content": "Who are you?"}] inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to("cuda") outputs = model.generate(**inputs, max_new_tokens=128) print(tokenizer.decode(outputs[0])) ```

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