Instructions to use jkim96/DeepSeek-R1-Distill-Qwen-32B-DASHQ-INT3-g64 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jkim96/DeepSeek-R1-Distill-Qwen-32B-DASHQ-INT3-g64 with Transformers:

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

pipe = pipeline("text-generation", model="jkim96/DeepSeek-R1-Distill-Qwen-32B-DASHQ-INT3-g64", 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("jkim96/DeepSeek-R1-Distill-Qwen-32B-DASHQ-INT3-g64", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("jkim96/DeepSeek-R1-Distill-Qwen-32B-DASHQ-INT3-g64", 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 jkim96/DeepSeek-R1-Distill-Qwen-32B-DASHQ-INT3-g64 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jkim96/DeepSeek-R1-Distill-Qwen-32B-DASHQ-INT3-g64"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jkim96/DeepSeek-R1-Distill-Qwen-32B-DASHQ-INT3-g64",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jkim96/DeepSeek-R1-Distill-Qwen-32B-DASHQ-INT3-g64

SGLang

How to use jkim96/DeepSeek-R1-Distill-Qwen-32B-DASHQ-INT3-g64 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 "jkim96/DeepSeek-R1-Distill-Qwen-32B-DASHQ-INT3-g64" \
    --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": "jkim96/DeepSeek-R1-Distill-Qwen-32B-DASHQ-INT3-g64",
		"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 "jkim96/DeepSeek-R1-Distill-Qwen-32B-DASHQ-INT3-g64" \
        --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": "jkim96/DeepSeek-R1-Distill-Qwen-32B-DASHQ-INT3-g64",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jkim96/DeepSeek-R1-Distill-Qwen-32B-DASHQ-INT3-g64 with Docker Model Runner:
```
docker model run hf.co/jkim96/DeepSeek-R1-Distill-Qwen-32B-DASHQ-INT3-g64
```

DeepSeek-R1-Distill-Qwen-32B-DASHQ-INT3-g64 / config.json

jkim96

Upload DASH-Q quantized checkpoint (INTNone, gNone)

3804d11 verified 30 days ago

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	{
	"architectures": [
	"DashQQwen2ForCausalLM"
	],
	"attention_dropout": 0.0,
	"auto_map": {
	"AutoModelForCausalLM": "modeling_dashq.DashQQwen2ForCausalLM"
	},
	"bos_token_id": 151643,
	"dashq": {
	"format": "dashq-packed-linear",
	"format_version": 1,
	"layer_metadata": "dashq_config.json",
	"method": "dashq",
	"n_quantized_modules": 448,
	"params": {
	"bits": 3,
	"group_size": 64,
	"low_memory_optimization": false,
	"moe_hessian_scope": "shared",
	"n_samples": 128,
	"scale_zero_dtype": "float16",
	"symmetric": false,
	"use_error_compensation": true,
	"use_optimal_shrinkage": true,
	"use_weighted_quantization": true
	}
	},
	"dtype": "bfloat16",
	"eos_token_id": 151643,
	"hidden_act": "silu",
	"hidden_size": 5120,
	"initializer_range": 0.02,
	"intermediate_size": 27648,
	"layer_types": [
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	"max_position_embeddings": 131072,
	"max_window_layers": 64,
	"model_type": "qwen2",
	"num_attention_heads": 40,
	"num_hidden_layers": 64,
	"num_key_value_heads": 8,
	"pad_token_id": null,
	"rms_norm_eps": 1e-05,
	"rope_parameters": {
	"rope_theta": 1000000.0,
	"rope_type": "default"
	},
	"sliding_window": null,
	"tie_word_embeddings": false,
	"transformers_version": "5.15.0",
	"use_cache": false,
	"use_sliding_window": false,
	"vocab_size": 152064
	}