Text Generation
Transformers
Safetensors
deepseek_v3
bf16
bfloat16
deepseek
v3-0324
conversational
custom_code
text-generation-inference
Instructions to use ModelCloud/DeepSeek-V3-0324-BF16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ModelCloud/DeepSeek-V3-0324-BF16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ModelCloud/DeepSeek-V3-0324-BF16", 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("ModelCloud/DeepSeek-V3-0324-BF16", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("ModelCloud/DeepSeek-V3-0324-BF16", 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=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ModelCloud/DeepSeek-V3-0324-BF16 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ModelCloud/DeepSeek-V3-0324-BF16" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ModelCloud/DeepSeek-V3-0324-BF16", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ModelCloud/DeepSeek-V3-0324-BF16
- SGLang
How to use ModelCloud/DeepSeek-V3-0324-BF16 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 "ModelCloud/DeepSeek-V3-0324-BF16" \ --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": "ModelCloud/DeepSeek-V3-0324-BF16", "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 "ModelCloud/DeepSeek-V3-0324-BF16" \ --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": "ModelCloud/DeepSeek-V3-0324-BF16", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use ModelCloud/DeepSeek-V3-0324-BF16 with Docker Model Runner:
docker model run hf.co/ModelCloud/DeepSeek-V3-0324-BF16
compat changes for transformers 4.51.0-dev
Browse filesTransformers added native DeepSeek V3 support in special branch but it require the config. values to be float
pip install git+https://github.com/huggingface/[email protected]
- config.json +3 -3
config.json
CHANGED
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@@ -36,9 +36,9 @@
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| 36 |
"qk_rope_head_dim": 64,
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"rms_norm_eps": 1e-06,
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| 38 |
"rope_scaling": {
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| 39 |
-
"beta_fast": 32,
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| 40 |
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"beta_slow": 1,
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| 41 |
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"factor": 40,
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"mscale": 1.0,
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"mscale_all_dim": 1.0,
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| 44 |
"original_max_position_embeddings": 4096,
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| 36 |
"qk_rope_head_dim": 64,
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| 37 |
"rms_norm_eps": 1e-06,
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| 38 |
"rope_scaling": {
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| 39 |
+
"beta_fast": 32.0,
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| 40 |
+
"beta_slow": 1.0,
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| 41 |
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"factor": 40.0,
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| 42 |
"mscale": 1.0,
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| 43 |
"mscale_all_dim": 1.0,
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| 44 |
"original_max_position_embeddings": 4096,
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