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ko120
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Model Details
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ko120
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Qwen/Qwen3.5-4B
Input Modalities
Output Modalities
Supported Functionality
Given a Python ML program, installed-package environment, data overview, and runtime budget, the model predicts the exception type, terminal output, validation metric, and whether a submission file exists. It uses the env_v1_reorder_excfirst_token prompt format:
text
<exc_type>EXC_None</exc_type><term_out>...</term_out><val_metric>...</val_metric><submission_exists>true</submission_exists>Use the system and human messages from the dataset for the exact input format. The matching tokenizer contains 82 ordinary exception tokens; these remain visible when decoding with skip_special_tokens=True.
Predictions estimate program behavior; real execution and grading were used to validate selected programs in the experiments.
The checkpoint was saved with Transformers 5.9.0. Use a Transformers version supporting Qwen3_5ForConditionalGeneration.
python
import torchfrom datasets import load_datasetfrom transformers import AutoTokenizer, Qwen3_5ForConditionalGeneration repo = "ko120/MLE-World-Qwen3.5-4B"tokenizer = AutoTokenizer.from_pretrained(repo)model = Qwen3_5ForConditionalGeneration.from_pretrained( repo, dtype=torch.bfloat16, device_map="auto")example = next(iter(load_dataset( "ko120/MLE-World", split="validation", streaming=True)))messages = [ {"role": "system", "content": example["system"]}, {"role": "user", "content": example["conversations"][0]["value"]},]prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=False,)inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to(model.device)with torch.inference_mode(): output = model.generate(**inputs, do_sample=False, max_new_tokens=8192, use_cache=True)print(tokenizer.decode(output[0, inputs.input_ids.shape[1]:], skip_special_tokens=True))Experiment inference used temperature 0, top_p=1, top_k=-1, min_p=0, repetition penalty 1, no presence/frequency penalties, seed 0, a 32,768-token serving context, and an 8,192-token output cap.
| Setting | Value |
|---|---|
| Method | Full SFT; vision tower and multimodal projector frozen |
| Training epochs / final step | 5 / 6685 |
| Learning rate | 1e-5 |
| Optimizer | Fused AdamW |
| Scheduler / warmup | Cosine / 5% |
| Packed sequence length | 12,288 tokens |
| Packing | Enabled, with neat packing |
| Effective batch | 128 packed sequences across 8 GPUs |
| Final evaluation loss | 0.0814168155 |
The evaluation loss is the training framework's token loss on the held-out corpus, with its packing and length processing. It is not execution-prediction accuracy.
training_config.original.yaml preserves the original training configuration and its local paths. Replace the model, dataset, and output paths for a new environment. Training logs and results are retained under training/.
The safetensors weights and tokenizer are copied from checkpoint 6685 without numerical conversion. The source stores 426 FP32 tensors and 297 BF16 tensors; its configuration declares BF16 for model loading. provenance.json records SHA-256 hashes, source checkpoint identity, tensor counts, and the experiment reference.
The model is distributed under the base model's Apache 2.0 license, included in LICENSE.
<exc_type>EXC_None</exc_type><term_out>...</term_out><val_metric>...</val_metric><submission_exists>true</submission_exists>import torchfrom datasets import load_datasetfrom transformers import AutoTokenizer, Qwen3_5ForConditionalGeneration repo = "ko120/MLE-World-Qwen3.5-4B"tokenizer = AutoTokenizer.from_pretrained(repo)model = Qwen3_5ForConditionalGeneration.from_pretrained( repo, dtype=torch.bfloat16, device_map="auto")example = next(iter(load_dataset( "ko120/MLE-World", split="validation", streaming=True)))messages = [ {"role": "system", "content": example["system"]}, {"role": "user", "content": example["conversations"][0]["value"]},]prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=False,)inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to(model.device)with torch.inference_mode(): output = model.generate(**inputs, do_sample=False, max_new_tokens=8192, use_cache=True)print(tokenizer.decode(output[0, inputs.input_ids.shape[1]:], skip_special_tokens=True))