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
```

echoctx commited on Sep 1

Commit

6204d65

verified ·

1 Parent(s): a809dcd

Add fair GPQA-Diamond + LiveCodeBench vs Qwen3.5-9B (thinking protocol) (README)

Browse files

Files changed (1) hide show

README.md +5 -2

README.md CHANGED Viewed

@@ -195,9 +195,10 @@ This model continues the **Qwen3.5** text architecture lineage (hybrid Teutonic
 ## Benchmarks
-See [`benchmarks.md`](./benchmarks.md) for the **corrected** same-harness comparison vs Qwen3.5-9B (chat + thinking on, fixed 2026-09-01). The first published Qwen column (MMLU-Pro ~21 / MATH ~20) was an **invalid harness artifact** (thinking ON + wrong chat template + short max_tokens → mid-think truncation).
 | Benchmark | Teutonic | Qwen3.5-9B (fixed harness) | Δ |
 |---|---:|---:|---:|
@@ -206,6 +207,8 @@ See [`benchmarks.md`](./benchmarks.md) for the **corrected** same-harness compar
 | HumanEval+ | **82.9** | 81.1 | **+1.8** |
 | MBPP+ | **75.1** | 64.6 | **+10.5** |
 | IFEval | **68.6** | 65.4 | **+3.2** |
 | RULER (4k–64k) | **89.5** | 88.4 | **+1.1** |
-Protocol: chat · thinking on · raised think budgets · seed 20260830. Vendor card numbers are separate; see benchmarks.md.

 ## Benchmarks
+See [`benchmarks.md`](./benchmarks.md) for the **corrected** same-harness comparison vs Qwen3.5-9B (chat + thinking on, fixed 2026-09-01). The first published Qwen column (MMLU-Pro ~21 / MATH ~20) was an **invalid harness artifact** (thinking ON with 1k–8k caps → mid-think truncation).
 | Benchmark | Teutonic | Qwen3.5-9B (fixed harness) | Δ |
 |---|---:|---:|---:|
 | HumanEval+ | **82.9** | 81.1 | **+1.8** |
 | MBPP+ | **75.1** | 64.6 | **+10.5** |
 | IFEval | **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** |
+Protocol: chat · thinking on · raised think budgets · seed 20260830. GPQA = generative boxed-letter (not card shuffle). See benchmarks.md.