Instructions to use amd/DeepSeek-V4-Pro-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use amd/DeepSeek-V4-Pro-NVFP4 with Transformers:

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

pipe = pipeline("text-generation", model="amd/DeepSeek-V4-Pro-NVFP4")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("amd/DeepSeek-V4-Pro-NVFP4")
model = AutoModelForCausalLM.from_pretrained("amd/DeepSeek-V4-Pro-NVFP4", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use amd/DeepSeek-V4-Pro-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "amd/DeepSeek-V4-Pro-NVFP4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amd/DeepSeek-V4-Pro-NVFP4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/amd/DeepSeek-V4-Pro-NVFP4

SGLang

How to use amd/DeepSeek-V4-Pro-NVFP4 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 "amd/DeepSeek-V4-Pro-NVFP4" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amd/DeepSeek-V4-Pro-NVFP4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "amd/DeepSeek-V4-Pro-NVFP4" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amd/DeepSeek-V4-Pro-NVFP4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use amd/DeepSeek-V4-Pro-NVFP4 with Docker Model Runner:
```
docker model run hf.co/amd/DeepSeek-V4-Pro-NVFP4
```

verified model upload

by huidongji - opened 2 days ago

base: refs/heads/main

←

from: refs/pr/3

Discussion Files changed

+21434

-24884

Files changed (9) hide show

README.md +1 -1
assets/dsv4_performance.png +3 -0
config.json +0 -0
inference/__pycache__/model.cpython-312.pyc +0 -0
inference/kernel.py +1 -1
inference/model.py +1 -2
input_scale.safetensors +3 -0
model-00064-of-00064.safetensors +2 -2
model.safetensors.index.json +2 -2

README.md CHANGED Viewed

@@ -115,7 +115,7 @@ pip install lm-eval[api]
 ```
 VLLM_ROCM_USE_AITER=1 \
 VLLM_ROCM_USE_AITER_MOE=1 \
-vllm serve amd/DeepSeek-V4-Pro-NVFP4 \
   --host localhost \
   --port 8001 \
   --dtype auto \

 ```
 VLLM_ROCM_USE_AITER=1 \
 VLLM_ROCM_USE_AITER_MOE=1 \
+vllm amd/DeepSeek-V4-Pro-NVFP4 \
   --host localhost \
   --port 8001 \
   --dtype auto \

assets/dsv4_performance.png ADDED Viewed

Git LFS Details

SHA256: 8fd472981a4c8d40c1845c51c5e8961fc4ef3ac22e7ec77801f534c239c1b30f
Pointer size: 132 Bytes
Size of remote file: 1 MB

config.json CHANGED Viewed

The diff for this file is too large to render. See raw diff

inference/__pycache__/model.cpython-312.pyc CHANGED Viewed

Binary files a/inference/__pycache__/model.cpython-312.pyc and b/inference/__pycache__/model.cpython-312.pyc differ

inference/kernel.py CHANGED Viewed

@@ -85,7 +85,7 @@ def act_quant_kernel(
                     for i, j in T.Parallel(blk_m, group_size):
                         y_local[i, j] = T.Cast(
                             out_dtype,
-                            T.Cast(compute_dtype, T.Cast(out_dtype, T.clamp(
                                 x_local[i, j] / s_local[i], fp8_min, fp8_max
                             ))) * s_local[i],
                         )

                     for i, j in T.Parallel(blk_m, group_size):
                         y_local[i, j] = T.Cast(
                             out_dtype,
+                            T.Cast(compute_dtype, T.Cast(FP8, T.clamp(
                                 x_local[i, j] / s_local[i], fp8_min, fp8_max
                             ))) * s_local[i],
                         )

inference/model.py CHANGED Viewed

@@ -624,8 +624,7 @@ class MoE(nn.Module):
         self.experts = nn.ModuleList([Expert(args.dim, args.moe_inter_dim, dtype=expert_dtype, swiglu_limit=args.swiglu_limit) if self.experts_start_idx <= i < self.experts_end_idx else None
                                        for i in range(self.n_routed_experts)])
         assert args.n_shared_experts == 1
-        # no swiglu_limit
-        self.shared_experts = Expert(args.dim, args.moe_inter_dim)
     def forward(self, x: torch.Tensor, input_ids: torch.Tensor) -> torch.Tensor:
         shape = x.size()

         self.experts = nn.ModuleList([Expert(args.dim, args.moe_inter_dim, dtype=expert_dtype, swiglu_limit=args.swiglu_limit) if self.experts_start_idx <= i < self.experts_end_idx else None
                                        for i in range(self.n_routed_experts)])
         assert args.n_shared_experts == 1
+        self.shared_experts = Expert(args.dim, args.moe_inter_dim, swiglu_limit=args.swiglu_limit)
     def forward(self, x: torch.Tensor, input_ids: torch.Tensor) -> torch.Tensor:
         shape = x.size()

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