morningstarxcdcode/adaption-no-robots-instructions-model

Adaption No Robots Instructions SFT 120B

LoRA adapter fine-tuned on the No Robots instruction-following dataset using Adaption's AutoScientist platform.

Model Details

• —Base model: togethercomputer/gpt-oss-120b-bf16 (120B parameter MoE, 128 experts, 4 per token)
• —Adapter: LoRA rank 4, alpha 8, targeting q_proj and v_proj
• —Training data: 10,000 human-written instruction-response pairs (No Robots dataset)
• —Training: 1 epoch, 22 steps, loss 2.22 → 1.40
• —Eval loss: 1.93 → 1.41

Training Results

How to Use

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model = AutoModelForCausalLM.from_pretrained(
    "togethercomputer/gpt-oss-120b-bf16",
    torch_dtype="bfloat16",
    device_map="auto"
)
model = PeftModel.from_pretrained(base_model, "morningstarxcdcode/adaption-no-robots-instructions-model")
tokenizer = AutoTokenizer.from_pretrained("morningstarxcdcode/adaption-no-robots-instructions-model")

inputs = tokenizer("Write a short story about a robot learning to cook.", return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Configuration

• —Optimizer: AdamW
• —Learning rate: 1e-4 with cosine decay
• —Batch size: 1
• —Max grad norm: 1.0
• —Warmup steps: 4

Team

Sourav Rajak, Priyanshu Tomar, Roshan G, Vivek Rajput

Part of the AutoScientist Challenge — Healthcare, Finance, Language, Legal, and Marketing tracks.