Edit model card

Qwen1.5-0.5B-Chat-GGUF

Original Model

Qwen/Qwen1.5-0.5B-Chat

Run with LlamaEdge

  • LlamaEdge version: v0.2.15 and above

  • Prompt template

    • Prompt type: chatml

    • Prompt string

      <|im_start|>system
      {system_message}<|im_end|>
      <|im_start|>user
      {prompt}<|im_end|>
      <|im_start|>assistant
      
  • Context size: 32000

  • Run as LlamaEdge service

    wasmedge --dir .:. --nn-preload default:GGML:AUTO:Qwen1.5-0.5B-Chat-Q5_K_M.gguf llama-api-server.wasm -p chatml
    
  • Run as LlamaEdge command app

    wasmedge --dir .:. --nn-preload default:GGML:AUTO:Qwen1.5-0.5B-Chat-Q5_K_M.gguf llama-chat.wasm -p chatml
    

Quantized GGUF Models

Name Quant method Bits Size Use case
Qwen1.5-0.5B-Chat-Q2_K.gguf Q2_K 2 298 MB smallest, significant quality loss - not recommended for most purposes
Qwen1.5-0.5B-Chat-Q3_K_L.gguf Q3_K_L 3 364 MB small, substantial quality loss
Qwen1.5-0.5B-Chat-Q3_K_M.gguf Q3_K_M 3 350 MB very small, high quality loss
Qwen1.5-0.5B-Chat-Q3_K_S.gguf Q3_K_S 3 333 MB very small, high quality loss
Qwen1.5-0.5B-Chat-Q4_0.gguf Q4_0 4 395 MB legacy; small, very high quality loss - prefer using Q3_K_M
Qwen1.5-0.5B-Chat-Q4_K_M.gguf Q4_K_M 4 407 MB medium, balanced quality - recommended
Qwen1.5-0.5B-Chat-Q4_K_S.gguf Q4_K_S 4 397 MB small, greater quality loss
Qwen1.5-0.5B-Chat-Q5_0.gguf Q5_0 5 453 MB legacy; medium, balanced quality - prefer using Q4_K_M
Qwen1.5-0.5B-Chat-Q5_K_M.gguf Q5_K_M 5 459 MB large, very low quality loss - recommended
Qwen1.5-0.5B-Chat-Q5_K_S.gguf Q5_K_S 5 453 MB large, low quality loss - recommended
Qwen1.5-0.5B-Chat-Q6_K.gguf Q6_K 6 515 MB very large, extremely low quality loss
Qwen1.5-0.5B-Chat-Q8_0.gguf Q8_0 8 665 MB very large, extremely low quality loss - not recommended
Downloads last month
268
GGUF
Model size
620M params
Architecture
qwen2

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Quantized from