Instructions to use jhyuckkim/DeepSeek-V2-Lite-Dense-3B-CP-K6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jhyuckkim/DeepSeek-V2-Lite-Dense-3B-CP-K6 with Transformers:

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

pipe = pipeline("text-generation", model="jhyuckkim/DeepSeek-V2-Lite-Dense-3B-CP-K6", 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("jhyuckkim/DeepSeek-V2-Lite-Dense-3B-CP-K6", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("jhyuckkim/DeepSeek-V2-Lite-Dense-3B-CP-K6", 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 jhyuckkim/DeepSeek-V2-Lite-Dense-3B-CP-K6 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jhyuckkim/DeepSeek-V2-Lite-Dense-3B-CP-K6"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jhyuckkim/DeepSeek-V2-Lite-Dense-3B-CP-K6",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jhyuckkim/DeepSeek-V2-Lite-Dense-3B-CP-K6

SGLang

How to use jhyuckkim/DeepSeek-V2-Lite-Dense-3B-CP-K6 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 "jhyuckkim/DeepSeek-V2-Lite-Dense-3B-CP-K6" \
    --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": "jhyuckkim/DeepSeek-V2-Lite-Dense-3B-CP-K6",
		"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 "jhyuckkim/DeepSeek-V2-Lite-Dense-3B-CP-K6" \
        --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": "jhyuckkim/DeepSeek-V2-Lite-Dense-3B-CP-K6",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jhyuckkim/DeepSeek-V2-Lite-Dense-3B-CP-K6 with Docker Model Runner:
```
docker model run hf.co/jhyuckkim/DeepSeek-V2-Lite-Dense-3B-CP-K6
```

DeepSeek-V2-Lite-Dense-3B-CP-K6

File size: 558 Bytes

{
  "teacher": "deepseek-ai/DeepSeek-V2-Lite",
  "dataset": "HuggingFaceFW/fineweb-edu",
  "dataset_config": "sample-10BT",
  "max_steps": 200,
  "temperature": 1.0,
  "teacher_top_k": null,
  "kl_direction": "forward",
  "intermediate_loss": false,
  "intermediate_loss_layers": null,
  "learning_rate": 0.0001,
  "per_device_batch_size": 4,
  "gradient_accumulation_steps": 24,
  "max_length": 4096,
  "final_loss": 11.8223446393013,
  "global_step": 200,
  "student_init": "CP pruned/merged dense student, see https://github.com/krafton-ai/moe-to-dense"
}