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
```

BM-TNG commited on Jul 6, 2025

Commit

903a224

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1 Parent(s): 5c70b2b

Update README.md - language typos

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@@ -42,8 +42,8 @@ R1T2 operates at a new sweet spot in intelligence vs. output token length. It ap
 **Recommendations for your model decision**
 *R1T2* compared...
-- *vs R1:* We hope that R1T2 is a very desirable, almost universal **better and drop-in replacement for R1**
-- *vs R1-0528:* R1T2 is a much **cheaper alternative to full R1-0528**, if the fullest 0528-level intelligence is not required
 - *vs R1T:* R1T2 is usually **recommended over R1T**, unless the specific personality of R1T was optimal, the think-token issue not important, or R1T's higher speed crucial
 - *vs V3-0324:* V3 is so much faster that if you can live with the **lower intelligence, take V3**, however, if you **need reasoning, R1T2** is the go-to model

 **Recommendations for your model decision**
 *R1T2* compared...
+- *vs R1:* We hope that R1T2 is a very desirable, almost universally **better drop-in replacement for R1**
+- *vs R1-0528:* R1T2 is a much **cheaper alternative to the full R1-0528**, if the full 0528-level intelligence is not required
 - *vs R1T:* R1T2 is usually **recommended over R1T**, unless the specific personality of R1T was optimal, the think-token issue not important, or R1T's higher speed crucial
 - *vs V3-0324:* V3 is so much faster that if you can live with the **lower intelligence, take V3**, however, if you **need reasoning, R1T2** is the go-to model