Instructions to use zjiayu064/DeepSeek-V2-Lite-BitsMoE-2bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zjiayu064/DeepSeek-V2-Lite-BitsMoE-2bit with Transformers:

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

pipe = pipeline("text-generation", model="zjiayu064/DeepSeek-V2-Lite-BitsMoE-2bit", 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("zjiayu064/DeepSeek-V2-Lite-BitsMoE-2bit", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("zjiayu064/DeepSeek-V2-Lite-BitsMoE-2bit", 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 zjiayu064/DeepSeek-V2-Lite-BitsMoE-2bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zjiayu064/DeepSeek-V2-Lite-BitsMoE-2bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zjiayu064/DeepSeek-V2-Lite-BitsMoE-2bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zjiayu064/DeepSeek-V2-Lite-BitsMoE-2bit

SGLang

How to use zjiayu064/DeepSeek-V2-Lite-BitsMoE-2bit 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 "zjiayu064/DeepSeek-V2-Lite-BitsMoE-2bit" \
    --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": "zjiayu064/DeepSeek-V2-Lite-BitsMoE-2bit",
		"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 "zjiayu064/DeepSeek-V2-Lite-BitsMoE-2bit" \
        --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": "zjiayu064/DeepSeek-V2-Lite-BitsMoE-2bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zjiayu064/DeepSeek-V2-Lite-BitsMoE-2bit with Docker Model Runner:
```
docker model run hf.co/zjiayu064/DeepSeek-V2-Lite-BitsMoE-2bit
```

zjiayu064 commited on 16 days ago

Commit

c91bd0e

verified ·

1 Parent(s): 1735d44

Update README.md

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README.md +6 -6

README.md CHANGED Viewed

@@ -18,10 +18,10 @@ pipeline_tag: text-generation
 # BitsMoE-DeepSeek-V2-Lite-2bit
-This repository provides a 2-bit quantized version of **DeepSeek-V2-Lite** using the BitsMoE quantization framework. The model was introduced in the paper [BitsMoE: Efficient Spectral Energy-Guided Bit Allocation for MoE LLM Quantization](https://huggingface.co/papers/2606.00079).
 The model is based on `deepseek-ai/DeepSeek-V2-Lite` and is intended for efficient inference of Mixture-of-Experts (MoE) large language models with significantly reduced memory footprint.
-- **Paper:** [BitsMoE: Efficient Spectral Energy-Guided Bit Allocation for MoE LLM Quantization](https://huggingface.co/papers/2606.00079)
 - **Code:** [GitHub - zjiayu064/BitsMoE](https://github.com/zjiayu064/BitsMoE)
 ## Model Details
@@ -29,7 +29,7 @@ The model is based on `deepseek-ai/DeepSeek-V2-Lite` and is intended for efficie
 - **Base model:** `deepseek-ai/DeepSeek-V2-Lite`
 - **Quantization:** 2-bit (Experts-Only)
 - **Architecture:** Mixture-of-Experts (MoE)
-- **Method:** BitsMoE, a spectral-energy-guided bit-allocation framework.
 ## Usage
@@ -58,13 +58,13 @@ This model follows the license of the original DeepSeek-V2-Lite model. Please re
 If you find our work useful, please consider citing:
 ```bibtex
-@misc{zhao2026bitsmoe,
-      title={{BitsMoE}: Efficient Spectral Energy-Guided Bit Allocation for {MoE} {LLM} Quantization},
       author={Jiayu Zhao and Zihan Teng and Minhao Fan and Tianrui Ma and Wentao Ren and Song Chen and Weichen Liu},
       year={2026},
       eprint={2606.00079},
       archivePrefix={arXiv},
       primaryClass={cs.LG},
-      url={https://arxiv.org/abs/2606.00079}
 }
 ```

 # BitsMoE-DeepSeek-V2-Lite-2bit
+This repository provides a 2-bit quantized version of **DeepSeek-V2-Lite** using the BitsMoE quantization framework. The model was introduced in the paper [BitsMoE: Cost-Aware Bit Allocation in Spectral Space for MoE LLM Quantization](https://huggingface.co/papers/2606.00079).
 The model is based on `deepseek-ai/DeepSeek-V2-Lite` and is intended for efficient inference of Mixture-of-Experts (MoE) large language models with significantly reduced memory footprint.
+- **Paper:** [BitsMoE: Cost-Aware Bit Allocation in Spectral Space for MoE LLM Quantization](https://huggingface.co/papers/2606.00079)
 - **Code:** [GitHub - zjiayu064/BitsMoE](https://github.com/zjiayu064/BitsMoE)
 ## Model Details
 - **Base model:** `deepseek-ai/DeepSeek-V2-Lite`
 - **Quantization:** 2-bit (Experts-Only)
 - **Architecture:** Mixture-of-Experts (MoE)
+- **Method:** BitsMoE, a cost-aware bit-allocation framework for MoE LLM quantization.
 ## Usage
 If you find our work useful, please consider citing:
 ```bibtex
+@misc{zhao2026bitsmoecostawarebitallocation,
+      title={BitsMoE: Cost-Aware Bit Allocation in Spectral Space for MoE LLM Quantization},
       author={Jiayu Zhao and Zihan Teng and Minhao Fan and Tianrui Ma and Wentao Ren and Song Chen and Weichen Liu},
       year={2026},
       eprint={2606.00079},
       archivePrefix={arXiv},
       primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2606.00079},
 }
 ```