Instructions to use tngtech/DeepSeek-TNG-R1T2-Chimera with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tngtech/DeepSeek-TNG-R1T2-Chimera with Transformers:

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

pipe = pipeline("text-generation", model="tngtech/DeepSeek-TNG-R1T2-Chimera", trust_remote_code=True)
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("tngtech/DeepSeek-TNG-R1T2-Chimera", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("tngtech/DeepSeek-TNG-R1T2-Chimera", trust_remote_code=True, 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 tngtech/DeepSeek-TNG-R1T2-Chimera with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tngtech/DeepSeek-TNG-R1T2-Chimera"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tngtech/DeepSeek-TNG-R1T2-Chimera",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tngtech/DeepSeek-TNG-R1T2-Chimera

SGLang

How to use tngtech/DeepSeek-TNG-R1T2-Chimera 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 "tngtech/DeepSeek-TNG-R1T2-Chimera" \
    --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": "tngtech/DeepSeek-TNG-R1T2-Chimera",
		"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 "tngtech/DeepSeek-TNG-R1T2-Chimera" \
        --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": "tngtech/DeepSeek-TNG-R1T2-Chimera",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tngtech/DeepSeek-TNG-R1T2-Chimera with Docker Model Runner:
```
docker model run hf.co/tngtech/DeepSeek-TNG-R1T2-Chimera
```

TNGHK commited on Aug 13, 2025

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@@ -68,6 +68,7 @@ We report measured benchmark results for our R1T2, R1T models and published benc
 | GPQA-Diamond                       | 77.9 | 72.0 |    68.4 | 71.5 |    81.0 |         |
 | Aider Polyglot                     | 64.4 | 48.4 |    44.9 | 52.0 |    71.6 | R1T2 source: Aider discord, t=0.75 |
 | EQ-Bench Longform Creative Writing | 76.4 |  ./. |    78.1 | 74.6 |    78.9 | see [EQ Bench](https://eqbench.com/creative_writing_longform.html)  |
 ## Technological background

 | GPQA-Diamond                       | 77.9 | 72.0 |    68.4 | 71.5 |    81.0 |         |
 | Aider Polyglot                     | 64.4 | 48.4 |    44.9 | 52.0 |    71.6 | R1T2 source: Aider discord, t=0.75 |
 | EQ-Bench Longform Creative Writing | 76.4 |  ./. |    78.1 | 74.6 |    78.9 | see [EQ Bench](https://eqbench.com/creative_writing_longform.html)  |
+| Vectara Hallucination Rate         |  5.5 |  ./. |     8.0 | 14.3 |     7.7 | see [Hallucination Leaderboard](https://github.com/vectara/hallucination-leaderboard), lower hallucination rates are better |
 ## Technological background