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XCombinator
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Model Details
Model Provider
XCombinator
Model Tree
Qwen/Qwen2.5-1.5B-Instruct
Input Modalities
Output Modalities
Supported Functionality
The model was trained on a unified JSON format: a system prompt that states the task + output schema, a numbered user sequence, and a single JSON answer:
{"reasoning": "...", "steps": ["STEP", ...]}{"reasoning": "...", "valid": true|false, "rule": "RULE_..."|null}Build the exact messages with zo_train.prompts.build_messages(task, item) from the
project repo, then apply the tokenizer's chat
template. Minimal next-step example:
python
from transformers import AutoModelForCausalLM, AutoTokenizer tok = AutoTokenizer.from_pretrained("XCombinator/sft-fab-instruct-all")model = AutoModelForCausalLM.from_pretrained("XCombinator/sft-fab-instruct-all", torch_dtype="auto") system = ( "You are a semiconductor wafer fabrication process-sequence assistant.\n" "TASK — Next-step prediction. Reply with one JSON object: " '{"reasoning": "...", "steps": ["BEST", "ALT2", ...]} (exact fab step names).')user = ( "Product family: MOSFET\n" "Partial sequence (numbered in execution order):\n" "1. RECEIVE WAFER LOT\n2. CLEAN WAFER\n3. GROW FIELD OXIDE\n4. COAT RESIST\n5. EXPOSE PATTERN\n\n" "Respond with the JSON object described in OUTPUT FORMAT.")msgs = [{"role": "system", "content": system}, {"role": "user", "content": user}]prompt = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)ids = tok(prompt, return_tensors="pt").to(model.device)out = model.generate(**ids, max_new_tokens=128, do_sample=False)print(tok.decode(out[0][ids["input_ids"].shape[1]:], skip_special_tokens=True))# -> {"reasoning": "", "steps": ["DEVELOP PHOTORESIST"]}Use the repo's zo-track / judge-eval harness for scored evaluation; pass
--model XCombinator/sft-fab-instruct-all --predictor hf.
| task | this model | n-gram baseline | frozen base |
|---|---|---|---|
| next-step (top-1) | 0.475 | 0.69 | ~0 |
| sequence completion (block-acc) | 0.555 | 0.637 | ~0 |
| anomaly (F1) | 0.567 | 0.89 | 0 |
The data-scaled sibling checkpoints push completion block-accuracy to 0.745 (beating the n-gram).
See the project repo + submissions/XCombinator/REPORT.md for the full study.
from_pretrained.from transformers import AutoModelForCausalLM, AutoTokenizer tok = AutoTokenizer.from_pretrained("XCombinator/sft-fab-instruct-all")model = AutoModelForCausalLM.from_pretrained("XCombinator/sft-fab-instruct-all", torch_dtype="auto") system = ( "You are a semiconductor wafer fabrication process-sequence assistant.\n" "TASK — Next-step prediction. Reply with one JSON object: " '{"reasoning": "...", "steps": ["BEST", "ALT2", ...]} (exact fab step names).')user = ( "Product family: MOSFET\n" "Partial sequence (numbered in execution order):\n" "1. RECEIVE WAFER LOT\n2. CLEAN WAFER\n3. GROW FIELD OXIDE\n4. COAT RESIST\n5. EXPOSE PATTERN\n\n" "Respond with the JSON object described in OUTPUT FORMAT.")msgs = [{"role": "system", "content": system}, {"role": "user", "content": user}]prompt = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)ids = tok(prompt, return_tensors="pt").to(model.device)out = model.generate(**ids, max_new_tokens=128, do_sample=False)print(tok.decode(out[0][ids["input_ids"].shape[1]:], skip_special_tokens=True))# -> {"reasoning": "", "steps": ["DEVELOP PHOTORESIST"]}