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Model: Abhinav-Anand/Two-And-A-Half-Qwen
Source: Original Platform

README.md

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+---
+license: apache-2.0
+base_model:
+  - Qwen/Qwen2.5-0.5B
+base_model_relation: quantized
+tags:
+  - quantization
+  - float16
+  - half-precision
+  - pytorch
+  - edge-deployment
+  - qwen2
+language:
+  - en
+pipeline_tag: text-generation
+---
+
+# Two_and_a_half_Qwen2.5-MiniFP16
+
+## Overview
+This is a **float16 (half precision) quantized** version of [Qwen/Qwen2.5-0.5B](https://huggingface.co/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
+
+```python
+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](https://huggingface.co/Qwen/Qwen2.5-0.5B)
+- **Parameters**: ~494M
+- **Architecture**: Qwen2 (decoder-only transformer)