DSR1-1.5B-llmc-awq-w4

AWQ quantized version of DeepSeek-R1-Distill-Qwen-1.5B using llm-compressor.

Model Details

• Base Model: DeepSeek-R1-Distill-Qwen-1.5B
• Quantization: AWQ W4A16 (4-bit weights, 16-bit activations)
• Group Size: 128
• Framework: llm-compressor
• Memory: 1.6GB (vs 3GB original)

Usage

vLLM

from vllm import LLM

model = LLM("edge-inference/DSR1-1.5B-llmc-awq-w4")
output = model.generate("Hello, how are you?")
print(output[0].outputs[0].text)

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("edge-inference/DSR1-1.5B-llmc-awq-w4")
tokenizer = AutoTokenizer.from_pretrained("edge-inference/DSR1-1.5B-llmc-awq-w4")

Performance

• Memory: 47% reduction (1.6GB vs 3GB)
• Speed: Faster inference due to reduced memory bandwidth
• Quality: Minimal degradation with AWQ quantization

License

Same as base model (DeepSeek License)