Instructions to use codefuse-ai/CodeFuse-DeepSeek-33B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use codefuse-ai/CodeFuse-DeepSeek-33B with Transformers:

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

pipe = pipeline("text-generation", model="codefuse-ai/CodeFuse-DeepSeek-33B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("codefuse-ai/CodeFuse-DeepSeek-33B")
model = AutoModelForCausalLM.from_pretrained("codefuse-ai/CodeFuse-DeepSeek-33B")
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 codefuse-ai/CodeFuse-DeepSeek-33B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "codefuse-ai/CodeFuse-DeepSeek-33B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "codefuse-ai/CodeFuse-DeepSeek-33B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/codefuse-ai/CodeFuse-DeepSeek-33B

SGLang

How to use codefuse-ai/CodeFuse-DeepSeek-33B 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 "codefuse-ai/CodeFuse-DeepSeek-33B" \
    --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": "codefuse-ai/CodeFuse-DeepSeek-33B",
		"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 "codefuse-ai/CodeFuse-DeepSeek-33B" \
        --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": "codefuse-ai/CodeFuse-DeepSeek-33B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use codefuse-ai/CodeFuse-DeepSeek-33B with Docker Model Runner:
```
docker model run hf.co/codefuse-ai/CodeFuse-DeepSeek-33B
```

codefuse-admin commited on Jan 16, 2024

Commit

f0f58cc

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1 Parent(s): 9a99284

Update README.md

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@@ -165,14 +165,13 @@ def load_model_tokenizer(model_path):
     tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)
     tokenizer.padding_side = "left"
-    model = AutoModelForCausalLM.from_pretrained(model_path, device_map='auto',torch_dtype=torch.bfloat16, trust_remote_code=True)
     return model, tokenizer
 HUMAN_ROLE_START_TAG = "<s>human\n"
 BOT_ROLE_START_TAG = "<s>bot\n"
-model_dir = snapshot_download('codefuse-ai/CodeFuse-DeepSeek-33B', revision='v1.0.0')
 text_list = [f'{HUMAN_ROLE_START_TAG}Write a QuickSort program\n#Python\n{BOT_ROLE_START_TAG}']
 model, tokenizer = load_model_tokenizer(model_dir)
@@ -351,14 +350,13 @@ def load_model_tokenizer(model_path):
     tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)
     tokenizer.padding_side = "left"
-    model = AutoModelForCausalLM.from_pretrained(model_path, device_map='auto',torch_dtype=torch.bfloat16, trust_remote_code=True)
     return model, tokenizer
 HUMAN_ROLE_START_TAG = "<s>human\n"
 BOT_ROLE_START_TAG = "<s>bot\n"
-model_dir = snapshot_download('codefuse-ai/CodeFuse-DeepSeek-33B', revision='v1.0.0')
 text_list = [f'{HUMAN_ROLE_START_TAG}请写一个快排程序\n#Python\n{BOT_ROLE_START_TAG}']
 model, tokenizer = load_model_tokenizer(model_dir)

     tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)
     tokenizer.padding_side = "left"
+    model = AutoModelForCausalLM.from_pretrained('codefuse-ai/CodeFuse-DeepSeek-33B', device_map='auto',torch_dtype=torch.bfloat16, trust_remote_code=True)
     return model, tokenizer
 HUMAN_ROLE_START_TAG = "<s>human\n"
 BOT_ROLE_START_TAG = "<s>bot\n"
 text_list = [f'{HUMAN_ROLE_START_TAG}Write a QuickSort program\n#Python\n{BOT_ROLE_START_TAG}']
 model, tokenizer = load_model_tokenizer(model_dir)
     tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)
     tokenizer.padding_side = "left"
+    model = AutoModelForCausalLM.from_pretrained('codefuse-ai/CodeFuse-DeepSeek-33B', device_map='auto',torch_dtype=torch.bfloat16, trust_remote_code=True)
     return model, tokenizer
 HUMAN_ROLE_START_TAG = "<s>human\n"
 BOT_ROLE_START_TAG = "<s>bot\n"
 text_list = [f'{HUMAN_ROLE_START_TAG}请写一个快排程序\n#Python\n{BOT_ROLE_START_TAG}']
 model, tokenizer = load_model_tokenizer(model_dir)