Instructions to use CortexLM/Teutonic-1-Chat-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CortexLM/Teutonic-1-Chat-Preview with Transformers:

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

pipe = pipeline("text-generation", model="CortexLM/Teutonic-1-Chat-Preview")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CortexLM/Teutonic-1-Chat-Preview")
model = AutoModelForCausalLM.from_pretrained("CortexLM/Teutonic-1-Chat-Preview", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use CortexLM/Teutonic-1-Chat-Preview with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CortexLM/Teutonic-1-Chat-Preview"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CortexLM/Teutonic-1-Chat-Preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CortexLM/Teutonic-1-Chat-Preview

SGLang

How to use CortexLM/Teutonic-1-Chat-Preview 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 "CortexLM/Teutonic-1-Chat-Preview" \
    --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": "CortexLM/Teutonic-1-Chat-Preview",
		"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 "CortexLM/Teutonic-1-Chat-Preview" \
        --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": "CortexLM/Teutonic-1-Chat-Preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use CortexLM/Teutonic-1-Chat-Preview with Docker Model Runner:
```
docker model run hf.co/CortexLM/Teutonic-1-Chat-Preview
```

Teutonic-1-Chat-Preview / benchmarks.md

echoctx

docs: add fair MMLU-Redux H2H (87.0 vs 91.3)

4227e09 verified about 1 month ago

preview code

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3 kB

	# Benchmarks (Teutonic-1-Chat-Preview) — corrected 2026-09-01

	Evaluated with harness `teutonic-eval-v1.2.0` after the thinking-budget / chat-template fix.
	Protocol: chat · thinking on · seed `20260830` · MATH-500 max_new=16k · HE+/MBPP+ max_new=8k · lm-eval `enable_thinking` + strip `</think>` · Qwen uses native `apply_chat_template`.
	Honesty: Prior HF Qwen column (MMLU-Pro 21 / MATH 20 / MBPP 8) was an invalid harness artifact — do not cite. Details: training-repo `reports/bench-harness-bug.md`.
	Residual risk: Teutonic MATH-500 still had 7.6% `finish=length` @16k; a generous re-check @64k (`20260901T133412Z`) scored 88.0 (vs 88.6) with 7.2% still truncated mid-think — no material uplift, scores below kept. Fair GPQA/LCB @32k still truncates heavily (GPQA Teu 52% / Qwen 43%; LCB Teu 55% / Qwen 68%) — absolute STEM/code scores remain conservative vs vendor cards.

	Head-to-head vs `Qwen/Qwen3.5-9B` on the same fixed harness. Vendor card = Qwen’s published stack (not identical protocol).

	## Same-harness deltas (Teutonic − Qwen3.5-9B) — corrected

	\| Benchmark \| Teutonic \| Qwen3.5-9B (fixed) \| Δ \|
	\|---\|---:\|---:\|---:\|
	\| MMLU-Pro (n=2000) \| 69.8 \| 73.2 \| −3.4 \|
	\| MMLU-Redux (generative, n=5330) \| 87.0 \| 91.3 \| −4.3 \|
	\| MATH-500 \| 88.6 \| 68.2 \| +20.4 \|
	\| HumanEval+ \| 82.9 \| 81.1 \| +1.8 \|
	\| MBPP+ \| 75.1 \| 64.6 \| +10.5 \|
	\| IFEval (prompt strict) \| 68.6 \| 65.4 \| +3.2 \|
	\| GPQA-Diamond (fair boxed) \| 46.0 \| 55.1 \| −9.1 \|
	\| LiveCodeBench (v5+v6) \| 26.9 \| 26.9 \| 0.0 \|
	\| RULER (4k–64k) \| 89.5 \| 88.4 \| +1.1 \|
	\| Needle \| 99.5 \| 100.0 \| −0.5 \|

	### Invalid prior Qwen column (do not use)

	\| Benchmark \| Broken harness \| Fixed \|
	\|---\|---:\|---:\|
	\| MMLU-Pro \| 21.3 \| 73.2 \|
	\| MATH-500 \| 20.2 \| 68.2 \|
	\| HumanEval+ \| 27.4 \| 81.1 \|
	\| MBPP+ \| 8.2 \| 64.6 \|
	\| IFEval \| 26.3 \| 65.4 \|

	## Knowledge & STEM

	\| Benchmark \| Teutonic \| Qwen3.5-9B (fixed harness) \| Qwen3.5-9B (card) \|
	\|---\|---:\|---:\|---:\|
	\| MMLU-Pro \| 69.8 \| 73.2 \| 82.5 \|
	\| MMLU-Redux \| 87.0‡ \| 91.3‡ \| 91.1 \|
	\| GPQA-Diamond \| 46.0† \| 55.1† \| 81.7 \|

	‡ lm-eval `mmlu_redux_generative` full (n=5330), chat + thinking ON + think-strip.
	† Fair generative boxed-letter (thinking ON, 32k, think-strip, fixed option order; n=198). Not card-comparable (no official shuffle). Truncation still high (~43–52% length) — absolute likely underestimated.

	## Math & code

	\| Benchmark \| Teutonic \| Qwen3.5-9B (fixed) \|
	\|---\|---:\|---:\|
	\| MATH-500 \| 88.6 \| 68.2 \|
	\| HumanEval+ \| 82.9 \| 81.1 \|
	\| MBPP+ \| 75.1 \| 64.6 \|
	\| LiveCodeBench (v5+v6) \| 26.9 \| 26.9 \|

	## Long context

	\| Benchmark \| Teutonic \| Qwen3.5-9B \|
	\|---\|---:\|---:\|
	\| RULER (4k–64k) \| 89.5 \| 88.4 \|
	\| Needle \| 99.5 \| 100.0 \|

	## Vision

	Teutonic is text-only — VL benchmarks are `N/A`. Qwen3.5-9B card (not comparable): MMMU 78.4 · MMMU-Pro 70.1 · MathVista 85.7.

	# Benchmarks (Teutonic-1-Chat-Preview) — corrected 2026-09-01

	Evaluated with harness `teutonic-eval-v1.2.0` after the thinking-budget / chat-template fix.
	Protocol: chat · thinking on · seed `20260830` · MATH-500 max_new=16k · HE+/MBPP+ max_new=8k · lm-eval `enable_thinking` + strip `</think>` · Qwen uses native `apply_chat_template`.
	Honesty: Prior HF Qwen column (MMLU-Pro 21 / MATH 20 / MBPP 8) was an invalid harness artifact — do not cite. Details: training-repo `reports/bench-harness-bug.md`.
	Residual risk: Teutonic MATH-500 still had 7.6% `finish=length` @16k; a generous re-check @64k (`20260901T133412Z`) scored 88.0 (vs 88.6) with 7.2% still truncated mid-think — no material uplift, scores below kept. Fair GPQA/LCB @32k still truncates heavily (GPQA Teu 52% / Qwen 43%; LCB Teu 55% / Qwen 68%) — absolute STEM/code scores remain conservative vs vendor cards.

	Head-to-head vs `Qwen/Qwen3.5-9B` on the same fixed harness. Vendor card = Qwen’s published stack (not identical protocol).

	## Same-harness deltas (Teutonic − Qwen3.5-9B) — corrected

	\| Benchmark \| Teutonic \| Qwen3.5-9B (fixed) \| Δ \|
	\|---\|---:\|---:\|---:\|
	\| MMLU-Pro (n=2000) \| 69.8 \| 73.2 \| −3.4 \|
	\| MMLU-Redux (generative, n=5330) \| 87.0 \| 91.3 \| −4.3 \|
	\| MATH-500 \| 88.6 \| 68.2 \| +20.4 \|
	\| HumanEval+ \| 82.9 \| 81.1 \| +1.8 \|
	\| MBPP+ \| 75.1 \| 64.6 \| +10.5 \|
	\| IFEval (prompt strict) \| 68.6 \| 65.4 \| +3.2 \|
	\| GPQA-Diamond (fair boxed) \| 46.0 \| 55.1 \| −9.1 \|
	\| LiveCodeBench (v5+v6) \| 26.9 \| 26.9 \| 0.0 \|
	\| RULER (4k–64k) \| 89.5 \| 88.4 \| +1.1 \|
	\| Needle \| 99.5 \| 100.0 \| −0.5 \|

	### Invalid prior Qwen column (do not use)

	\| Benchmark \| Broken harness \| Fixed \|
	\|---\|---:\|---:\|
	\| MMLU-Pro \| 21.3 \| 73.2 \|
	\| MATH-500 \| 20.2 \| 68.2 \|
	\| HumanEval+ \| 27.4 \| 81.1 \|
	\| MBPP+ \| 8.2 \| 64.6 \|
	\| IFEval \| 26.3 \| 65.4 \|

	## Knowledge & STEM

	\| Benchmark \| Teutonic \| Qwen3.5-9B (fixed harness) \| Qwen3.5-9B (card) \|
	\|---\|---:\|---:\|---:\|
	\| MMLU-Pro \| 69.8 \| 73.2 \| 82.5 \|
	\| MMLU-Redux \| 87.0‡ \| 91.3‡ \| 91.1 \|
	\| GPQA-Diamond \| 46.0† \| 55.1† \| 81.7 \|

	‡ lm-eval `mmlu_redux_generative` full (n=5330), chat + thinking ON + think-strip.
	† Fair generative boxed-letter (thinking ON, 32k, think-strip, fixed option order; n=198). Not card-comparable (no official shuffle). Truncation still high (~43–52% length) — absolute likely underestimated.

	## Math & code

	\| Benchmark \| Teutonic \| Qwen3.5-9B (fixed) \|
	\|---\|---:\|---:\|
	\| MATH-500 \| 88.6 \| 68.2 \|
	\| HumanEval+ \| 82.9 \| 81.1 \|
	\| MBPP+ \| 75.1 \| 64.6 \|
	\| LiveCodeBench (v5+v6) \| 26.9 \| 26.9 \|

	## Long context

	\| Benchmark \| Teutonic \| Qwen3.5-9B \|
	\|---\|---:\|---:\|
	\| RULER (4k–64k) \| 89.5 \| 88.4 \|
	\| Needle \| 99.5 \| 100.0 \|

	## Vision

	Teutonic is text-only — VL benchmarks are `N/A`. Qwen3.5-9B card (not comparable): MMMU 78.4 · MMMU-Pro 70.1 · MathVista 85.7.