Instructions to use RedHatAI/DeepSeek-R1-Distill-Qwen-7B-quantized.w4a16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RedHatAI/DeepSeek-R1-Distill-Qwen-7B-quantized.w4a16 with Transformers:

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

pipe = pipeline("text-generation", model="RedHatAI/DeepSeek-R1-Distill-Qwen-7B-quantized.w4a16")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RedHatAI/DeepSeek-R1-Distill-Qwen-7B-quantized.w4a16")
model = AutoModelForCausalLM.from_pretrained("RedHatAI/DeepSeek-R1-Distill-Qwen-7B-quantized.w4a16", 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 RedHatAI/DeepSeek-R1-Distill-Qwen-7B-quantized.w4a16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RedHatAI/DeepSeek-R1-Distill-Qwen-7B-quantized.w4a16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RedHatAI/DeepSeek-R1-Distill-Qwen-7B-quantized.w4a16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RedHatAI/DeepSeek-R1-Distill-Qwen-7B-quantized.w4a16

SGLang

How to use RedHatAI/DeepSeek-R1-Distill-Qwen-7B-quantized.w4a16 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 "RedHatAI/DeepSeek-R1-Distill-Qwen-7B-quantized.w4a16" \
    --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": "RedHatAI/DeepSeek-R1-Distill-Qwen-7B-quantized.w4a16",
		"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 "RedHatAI/DeepSeek-R1-Distill-Qwen-7B-quantized.w4a16" \
        --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": "RedHatAI/DeepSeek-R1-Distill-Qwen-7B-quantized.w4a16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use RedHatAI/DeepSeek-R1-Distill-Qwen-7B-quantized.w4a16 with Docker Model Runner:
```
docker model run hf.co/RedHatAI/DeepSeek-R1-Distill-Qwen-7B-quantized.w4a16
```

kylesayrs commited on 27 days ago

Commit

83b6840

verified ·

1 Parent(s): 9559a91

Remove sparsity_config fields from config.json

Browse files

Files changed (1) hide show

config.json +2 -208

config.json CHANGED Viewed

@@ -46,213 +46,7 @@
     "kv_cache_scheme": null,
     "quant_method": "compressed-tensors",
     "quantization_status": "compressed",
-    "sparsity_config": {
-      "format": "dense",
-      "global_sparsity": 0.13432298565034687,
-      "ignore": [
-        "model.layers.0.self_attn.q_proj",
-        "model.layers.0.self_attn.k_proj",
-        "model.layers.0.self_attn.v_proj",
-        "model.layers.0.self_attn.o_proj",
-        "model.layers.0.mlp.gate_proj",
-        "model.layers.0.mlp.up_proj",
-        "model.layers.0.mlp.down_proj",
-        "model.layers.1.self_attn.q_proj",
-        "model.layers.1.self_attn.k_proj",
-        "model.layers.1.self_attn.v_proj",
-        "model.layers.1.self_attn.o_proj",
-        "model.layers.1.mlp.gate_proj",
-        "model.layers.1.mlp.up_proj",
-        "model.layers.2.self_attn.q_proj",
-        "model.layers.2.self_attn.k_proj",
-        "model.layers.2.self_attn.v_proj",
-        "model.layers.2.self_attn.o_proj",
-        "model.layers.2.mlp.gate_proj",
-        "model.layers.2.mlp.up_proj",
-        "model.layers.2.mlp.down_proj",
-        "model.layers.3.self_attn.q_proj",
-        "model.layers.3.self_attn.k_proj",
-        "model.layers.3.self_attn.v_proj",
-        "model.layers.3.self_attn.o_proj",
-        "model.layers.3.mlp.gate_proj",
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-        "model.layers.21.self_attn.o_proj",
-        "model.layers.21.mlp.gate_proj",
-        "model.layers.21.mlp.up_proj",
-        "model.layers.21.mlp.down_proj",
-        "model.layers.22.self_attn.q_proj",
-        "model.layers.22.self_attn.k_proj",
-        "model.layers.22.self_attn.v_proj",
-        "model.layers.22.self_attn.o_proj",
-        "model.layers.22.mlp.gate_proj",
-        "model.layers.22.mlp.up_proj",
-        "model.layers.22.mlp.down_proj",
-        "model.layers.23.self_attn.q_proj",
-        "model.layers.23.self_attn.k_proj",
-        "model.layers.23.self_attn.v_proj",
-        "model.layers.23.self_attn.o_proj",
-        "model.layers.23.mlp.gate_proj",
-        "model.layers.23.mlp.up_proj",
-        "model.layers.23.mlp.down_proj",
-        "model.layers.24.self_attn.q_proj",
-        "model.layers.24.self_attn.k_proj",
-        "model.layers.24.self_attn.v_proj",
-        "model.layers.24.self_attn.o_proj",
-        "model.layers.24.mlp.gate_proj",
-        "model.layers.24.mlp.up_proj",
-        "model.layers.24.mlp.down_proj",
-        "model.layers.25.self_attn.q_proj",
-        "model.layers.25.self_attn.k_proj",
-        "model.layers.25.self_attn.v_proj",
-        "model.layers.25.self_attn.o_proj",
-        "model.layers.25.mlp.gate_proj",
-        "model.layers.25.mlp.up_proj",
-        "model.layers.25.mlp.down_proj",
-        "model.layers.26.self_attn.q_proj",
-        "model.layers.26.self_attn.k_proj",
-        "model.layers.26.self_attn.v_proj",
-        "model.layers.26.self_attn.o_proj",
-        "model.layers.26.mlp.gate_proj",
-        "model.layers.26.mlp.up_proj",
-        "model.layers.26.mlp.down_proj",
-        "model.layers.27.self_attn.q_proj",
-        "model.layers.27.self_attn.k_proj",
-        "model.layers.27.self_attn.v_proj",
-        "model.layers.27.self_attn.o_proj",
-        "model.layers.27.mlp.gate_proj",
-        "model.layers.27.mlp.up_proj",
-        "model.layers.27.mlp.down_proj",
-        "lm_head"
-      ],
-      "registry_requires_subclass": false,
-      "sparsity_structure": "unstructured",
-      "targets": [
-        "model.layers.1.mlp.down_proj"
-      ]
-    }
   },
   "rms_norm_eps": 1e-06,
   "rope_scaling": null,
@@ -265,4 +59,4 @@
   "use_mrope": false,
   "use_sliding_window": false,
   "vocab_size": 152064
-}

     "kv_cache_scheme": null,
     "quant_method": "compressed-tensors",
     "quantization_status": "compressed",
+    "sparsity_config": {}
   },
   "rms_norm_eps": 1e-06,
   "rope_scaling": null,
   "use_mrope": false,
   "use_sliding_window": false,
   "vocab_size": 152064
+}