Instructions to use XCombinator/sft-fab-scale-100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use XCombinator/sft-fab-scale-100 with Transformers:

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

pipe = pipeline("text-generation", model="XCombinator/sft-fab-scale-100")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XCombinator/sft-fab-scale-100")
model = AutoModelForCausalLM.from_pretrained("XCombinator/sft-fab-scale-100")
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-scale-100 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "XCombinator/sft-fab-scale-100"
# 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-scale-100",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/XCombinator/sft-fab-scale-100

SGLang

How to use XCombinator/sft-fab-scale-100 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-scale-100" \
    --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-scale-100",
		"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-scale-100" \
        --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-scale-100",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use XCombinator/sft-fab-scale-100 with Docker Model Runner:
```
docker model run hf.co/XCombinator/sft-fab-scale-100
```

gardan4 commited on May 31

Commit

8daad3f

verified ·

1 Parent(s): f644688

Add model card + post-deadline notice

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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 — sft-fab-scale-100
+> ⚠️ **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 training.
+Full fine-tune of **Qwen/Qwen2.5-1.5B-Instruct** on semiconductor wafer-fab **process logic** (Zero One Hack_01,
+Industrial AI / Infineon track), team **XCombinator**. Data-scaling point — **100 routes/family**, 1 epoch.
+One of the checkpoints compared in our study; the flagship is
+[`XCombinator/sft-fab-instruct-all`](https://huggingface.co/XCombinator/sft-fab-instruct-all).
+## Prompt format
+Unified JSON format: a system prompt (task + output schema) + a numbered user sequence → one JSON
+answer (`{"reasoning": "...", "steps": [...]}` for next-step/completion; `{"reasoning": "...",
+"valid": bool, "rule": "RULE_..."|null}` for anomaly). Build the exact messages with
+`zo_train.prompts.build_messages` from the
+[project repo](https://github.com/gardan4/Zero-One-XCombinator), then apply the tokenizer chat
+template. See the flagship model card for a full `from_pretrained` snippet.
+## Evaluation (MOSFET labeled eval, n≈200)
+| task | this checkpoint | n-gram baseline |
+|---|---|---|
+| next-step (top-1) | 0.365 | 0.69 |
+| sequence completion (block-acc) | 0.345 | 0.637 |
+| anomaly (F1) | 0.000 | 0.89 |
+Full study + all checkpoints: the project repo and `submissions/XCombinator/REPORT.md`.
+## Notes
+- Full fine-tune (not a LoRA adapter) — loads directly with `AutoModelForCausalLM.from_pretrained`.
+- Trained on Leonardo (CINECA) A100 via a deterministic data factory over the organizer grammar.