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

Raw History Blame Contribute Delete

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.