Instructions to use z-lab/gemma-4-31B-it-DFlash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use z-lab/gemma-4-31B-it-DFlash with Transformers:

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

pipe = pipeline("text-generation", model="z-lab/gemma-4-31B-it-DFlash")

# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("z-lab/gemma-4-31B-it-DFlash")
model = AutoModel.from_pretrained("z-lab/gemma-4-31B-it-DFlash")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use z-lab/gemma-4-31B-it-DFlash with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "z-lab/gemma-4-31B-it-DFlash"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "z-lab/gemma-4-31B-it-DFlash",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/z-lab/gemma-4-31B-it-DFlash

SGLang

How to use z-lab/gemma-4-31B-it-DFlash 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 "z-lab/gemma-4-31B-it-DFlash" \
    --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": "z-lab/gemma-4-31B-it-DFlash",
		"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 "z-lab/gemma-4-31B-it-DFlash" \
        --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": "z-lab/gemma-4-31B-it-DFlash",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use z-lab/gemma-4-31B-it-DFlash with Docker Model Runner:
```
docker model run hf.co/z-lab/gemma-4-31B-it-DFlash
```

jianchen0311 commited on 16 days ago

Commit

f3b070b

verified ·

1 Parent(s): 47a40a9

Upload folder using huggingface_hub

Browse files

Files changed (2) hide show

config.json +52 -0
model.safetensors +3 -0

config.json ADDED Viewed

	@@ -0,0 +1,52 @@

+{
+  "architectures": [
+    "DFlashDraftModel"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "block_size": 16,
+  "bos_token_id": 2,
+  "dflash_config": {
+    "mask_token_id": 4,
+    "target_layer_ids": [
+      1,
+      12,
+      23,
+      35,
+      46,
+      57
+    ]
+  },
+  "dtype": "bfloat16",
+  "eos_token_id": 1,
+  "final_logit_softcapping": 30.0,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 5376,
+  "initializer_range": 0.02,
+  "intermediate_size": 10752,
+  "layer_types": [
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 262144,
+  "max_window_layers": 5,
+  "model_type": "qwen3",
+  "num_attention_heads": 64,
+  "num_hidden_layers": 5,
+  "num_key_value_heads": 8,
+  "num_target_layers": 60,
+  "pad_token_id": 0,
+  "rms_norm_eps": 1e-06,
+  "sliding_window": 2048,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.6.0",
+  "use_cache": true,
+  "use_sliding_window": true,
+  "vocab_size": 262144,
+  "rope_theta": 1000000,
+  "rope_scaling": null
+}

model.safetensors ADDED Viewed

	@@ -0,0 +1,3 @@

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+oid sha256:54d42a8f36b4f92dbabf6c16a10cc4829c3c5f6b437e75f4bec80cf81bdcc4cb
+size 3071941240