license, base_model, base_model_relation, tags, language, pipeline_tag

license	base_model	base_model_relation	tags	language	pipeline_tag
apache-2.0	Qwen/Qwen2.5-0.5B	quantized	quantization float16 half-precision pytorch edge-deployment qwen2	en	text-generation

Two_and_a_half_Qwen2.5-MiniFP16

Overview

This is a float16 (half precision) quantized version of Qwen/Qwen2.5-0.5B. All model weights are converted from float32 to float16, reducing model size by ~50% while maintaining near-identical text generation quality.

Key Features

• Half the size: 942.4 MB (down from 1884.7 MB)
• No GPU required: Runs on CPU and Apple Silicon Macs
• Near-lossless: Float16 preserves most of the original precision
• Zero training: Pure post-training quantization
• HuggingFace native: Standard safetensors format, load with AutoModelForCausalLM

Quantization Details

• Method: PyTorch .half() conversion (float32 -> float16)
• Target: All model parameters (weights, biases, embeddings)
• Original dtype: torch.float32 (32-bit, 4 bytes per weight)
• Quantized dtype: torch.float16 (16-bit, 2 bytes per weight)
• Compression ratio: ~2x

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

tokenizer = AutoTokenizer.from_pretrained("Ringkvist/Two_and_a_half_Qwen2.5-MiniFP16")
model = AutoModelForCausalLM.from_pretrained(
    "Ringkvist/Two_and_a_half_Qwen2.5-MiniFP16",
    torch_dtype=torch.float16,
)

inputs = tokenizer("The future of AI is", return_tensors="pt")
with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=100, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Limitations

• Slight numerical precision loss vs float32 (negligible for inference)
• Some operations may need float32 upcasting on certain hardware
• Not as aggressive as int8/int4 quantization but much simpler and more portable

Base Model

• Model: Qwen/Qwen2.5-0.5B
• Parameters: ~494M
• Architecture: Qwen2 (decoder-only transformer)