Instructions to use XCombinator/sft-fab-instruct-all with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use XCombinator/sft-fab-instruct-all with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="XCombinator/sft-fab-instruct-all")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XCombinator/sft-fab-instruct-all")
model = AutoModelForCausalLM.from_pretrained("XCombinator/sft-fab-instruct-all")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use XCombinator/sft-fab-instruct-all with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "XCombinator/sft-fab-instruct-all"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "XCombinator/sft-fab-instruct-all",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/XCombinator/sft-fab-instruct-all

SGLang

How to use XCombinator/sft-fab-instruct-all with 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 "XCombinator/sft-fab-instruct-all" \
    --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": "XCombinator/sft-fab-instruct-all",
		"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 "XCombinator/sft-fab-instruct-all" \
        --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": "XCombinator/sft-fab-instruct-all",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use XCombinator/sft-fab-instruct-all with Docker Model Runner:
```
docker model run hf.co/XCombinator/sft-fab-instruct-all
```

sft-fab-instruct-all / README.md

gardan4

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	---
	license: apache-2.0
	base_model: Qwen/Qwen2.5-1.5B-Instruct
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- semiconductor
	- fab-process
	- infineon
	- industrial-ai
	- sequence-modeling
	---

	# XCombinator — Fab Process model (SFT, all-family)

	> ⚠️ Post-deadline upload notice. This Hugging Face repository was *published after* the
	> Zero One Hack_01 submission deadline (2026-05-31 10:00 CET)**, solely to give judges download
	> access. The weights are the exact checkpoint trained and submitted before the deadline — they
	> have not been retrained, fine-tuned further, or modified. Only the act of uploading/hosting
	> happened after the deadline. File timestamps reflect the upload, not the training.

	Full fine-tune of Qwen/Qwen2.5-1.5B-Instruct on semiconductor wafer-fab process logic for the
	Zero One Hack_01 Industrial AI (Infineon) track. One promptable model for all three graded tasks:
	next-step prediction, sequence completion, and anomaly (rule-violation) detection, over a
	fixed ~120-step uppercase fab vocabulary across three product families (MOSFET / IGBT / IC).

	This is the team XCombinator headline checkpoint (`sft-instruct-all`).

	## Prompt format (important)

	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:

	- next-step / completion → `{"reasoning": "...", "steps": ["STEP", ...]}`
	- anomaly → `{"reasoning": "...", "valid": true\|false, "rule": "RULE_..."\|null}`

	Build the exact messages with `zo_train.prompts.build_messages(task, item)` from the
	[project repo](https://github.com/gardan4/Zero-One-XCombinator), 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`.

	## Evaluation (MOSFET labeled eval, n≈200)

	\| 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.

	## Notes

	- Full fine-tune (not a LoRA adapter) — loads directly with `from_pretrained`.
	- Trained on Leonardo (CINECA) A100; deterministic data factory over the organizer grammar.