Instructions to use Alphatao/Affine-0000000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Alphatao/Affine-0000000 with Transformers:

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

pipe = pipeline("text-generation", model="Alphatao/Affine-0000000", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Alphatao/Affine-0000000", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Alphatao/Affine-0000000", trust_remote_code=True)
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Alphatao/Affine-0000000 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Alphatao/Affine-0000000"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Alphatao/Affine-0000000",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Alphatao/Affine-0000000

SGLang

How to use Alphatao/Affine-0000000 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 "Alphatao/Affine-0000000" \
    --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": "Alphatao/Affine-0000000",
		"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 "Alphatao/Affine-0000000" \
        --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": "Alphatao/Affine-0000000",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Alphatao/Affine-0000000 with Docker Model Runner:
```
docker model run hf.co/Alphatao/Affine-0000000
```

Affine-0000000 / README.md

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	---
	license: mit
	library_name: transformers
	base_model:
	- deepseek-ai/DeepSeek-V3-0324
	- deepseek-ai/DeepSeek-R1
	pipeline_tag: text-generation
	---
	# DeepSeek-R1T-Chimera

	<div align="center">
	<img src="https://354918363417-runtime-assets.s3.eu-central-1.amazonaws.com/company_logo_light.svg"
	alt="TNG Logo"
	width="400"
	style="display: inline-block; vertical-align: middle;"/>
	</div>
	<br>
	<div align="center">
	<a href="LICENSE" style="margin: 2px;">
	<img alt="License" src="https://img.shields.io/badge/License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
	</a>
	</div>
	<br>
	<div align="center">
	<a href="https://x.com/tngtech/status/1916284566127444468" style="margin: 2px;">
	<img alt="Benchmarks" src="R1T-Chimera_Benchmarks_20250427_V1.jpg" style="display: inline-block; vertical-align: middle;"/>
	</a>
	</div>


	Model merge of DeepSeek-R1 and DeepSeek-V3 (0324)

	An open weights model combining the intelligence of R1 with the token efficiency of V3.

	For details on the construction process and analyses of Chimera model variants, please [read our paper](https://arxiv.org/abs/2506.14794).

	[Paper on arXiV](https://arxiv.org/abs/2506.14794) \| [Announcement on X](https://x.com/tngtech/status/1916284566127444468) \| [LinkedIn post](https://www.linkedin.com/posts/tng-technology-consulting_on-the-weekend-we-released-deepseek-r1t-chimera-activity-7323008947236290560-Cf2m) \| [Try it on OpenRouter](https://openrouter.ai/tngtech/deepseek-r1t-chimera:free)


	## Model Details

	- Architecture: DeepSeek-MoE Transformer-based language model
	- Combination Method: Merged model weights from DeepSeek-R1 and DeepSeek-V3 (0324)
	- Release Date: 2025-04-27

	## Use, Out-of-scope Use, Limitations, Risks, Recommendations et al
	Regarding R1T Chimera, we ask you to follow the careful guidelines that Microsoft has created for their "MAI-DS-R1" DeepSeek-based model.

	These guidelines are available [here on Hugging Face](https://huggingface.co/microsoft/MAI-DS-R1).

	## Contact

	- Email: research@tngtech.com
	- X.com: @tngtech

	## Citation

	```
	@misc{tng_technology_consulting_gmbh_2025,
	author = { TNG Technology Consulting GmbH },
	title = { DeepSeek-R1T-Chimera },
	year = 2025,
	month = {April},
	url = { https://huggingface.co/tngtech/DeepSeek-R1T-Chimera },
	doi = { 10.57967/hf/5330 },
	publisher = { Hugging Face }
	}

	```