Instructions to use deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct", 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("deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct", 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 Settings

vLLM

How to use deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct

SGLang

How to use deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct 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 "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct" \
    --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": "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct",
		"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 "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct" \
        --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": "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct with Docker Model Runner:
```
docker model run hf.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct
```

llama_cpp compatible?

by Techn0man1ac - opened Jul 7, 2024

Discussion

Techn0man1ac

Jul 7, 2024

I use this code:

from llama_cpp import Llama

Device = "cuda:0"
user_prompt = "Ви шанобливий помічник, розмовляєте українською мовою, коротко та лаконічно. Привітайтесь та запитайте як зовуть співрозмовника."

model = Llama(
model_path="D:\lm-studio\models\bartowski\DeepSeek-Coder-V2-Lite-Instruct-GGUF\DeepSeek-Coder-V2-Lite-Instruct-Q8_0.gguf",
#chat_format="deepseek2",
n_gpu_layers=5,
#flash_attn=True,
n_threads=6,
n_ctx=8192,
device=Device,
verbose=True,) # verbose=False - debug output off

messages=[{"role": "system", "content": user_prompt}]

def AI_speack(userText: str): #-> str:
new_message = {"role": "user", "content": userText}
messages.append(new_message)

output = model.create_chat_completion(messages, temperature=0.5, max_tokens=1024, stream=True)

LLM_Responce = ""

for chunk in output:
    delta = chunk["choices"][0]["delta"]
    if "content" not in delta:
        continue
    print(delta["content"], end="", flush=True)
    LLM_Responce += delta["content"]
print()


new_message = {"role": "assistant", "content": LLM_Responce}
messages.append(new_message)

#print(messages)
#return LLM_Responce

while True:
print(AI_speack(input(">")))

And answer is same, like "GGGGGGGGGGGGGGGGGG..."

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