{
  "job_id": "14858223-3e80-4cf2-96b3-823bcb55e541",
  "training_experiment_id": "f3a9de60-aed3-4192-8393-7c9b3bdf8d0f",
  "original_model_name": "openai/gpt-oss-120b",
  "trained_model_name": "adaption_emir_reporting_code_gen",
  "training_method": "sft",
  "training_type": "lora",
  "data_format": "chat",
  "hyperparams": {
    "lora": "true",
    "lora_r": 64,
    "n_evals": 5,
    "n_epochs": 4,
    "batch_size": "max",
    "lora_alpha": 128,
    "lora_dropout": 0,
    "min_lr_ratio": 0.1,
    "warmup_ratio": 0.05,
    "weight_decay": 0.01,
    "learning_rate": 0.0003,
    "max_grad_norm": 1,
    "base_model_size": "120B",
    "train_on_inputs": "false",
    "training_method": "sft",
    "lr_scheduler_type": "cosine",
    "scheduler_num_cycles": 0.5,
    "lora_trainable_modules": "q_proj,k_proj,v_proj,o_proj"
  }
}

Training Data

The model was trained on 51,803 rows of adapted data with the following domain distribution: code (79%), legal (17%), corporate-business (2%), data-analysis-visualization (1%), academic-education (0%), math (0%), science (0%).

Model Evaluation

The model was evaluated on an in-distribution held-out test set as well as a broader domain-specific test set to measure generalization.

Domain	Win rate vs. base model
code	34%

How to use

pip install torch transformers peft

python

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

BASE = "openai/gpt-oss-120b"
ADAPTER = "<this-repo-id>"

device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float32 if device == "cpu" else torch.bfloat16

base = AutoModelForCausalLM.from_pretrained(BASE, dtype=dtype).to(device)
model = PeftModel.from_pretrained(base, ADAPTER)
# Optional: merge the LoRA weights into the base for faster inference
model = model.merge_and_unload()
model.eval()

tokenizer = AutoTokenizer.from_pretrained(BASE)
messages = [{"role": "user", "content": "Hello!"}]
text = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(device)

with torch.inference_mode():
    out = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Tonic/adaption_emir_reporting_code_gen

adaption_emir_reporting_code_gen

Model Training

AutoScientist Config

Training Data

Model Evaluation

How to use

Tonic/
adaption_emir_reporting_code_gen