safetensors mistral license:apache-2.0 4-bit bitsandbytes region:us
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license: apache-2.0
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## Model Card for `zephyr-7b-beta-quantized`

### Model Details
- **Model Name**: `dhanasekharB/Zephyr-7B-quantized (Quantized)`
- **Repository**: dhanasekharB/Zephyr-7B-quantized
- **Quantization**: 4-bit quantized using `bitsandbytes`
- **Intended Use**: General-purpose causal language model with efficient inference on CPUs due to quantization
- **Model Type**: Auto-regressive Transformer for text generation

### Model Description
This quantized version of `HuggingFaceH4/zephyr-7b-beta` is optimized for low-memory usage and fast inference on CPUs. By reducing the model size through 4-bit quantization, it’s suitable for applications that require text generation on resource-constrained devices.

Quantization was performed using the `bitsandbytes` library, specifically with 4-bit NormalFloat quantization (`nf4`). This model supports tasks like text generation and interactive dialogue, and is tailored for efficient, on-device deployment.

### Usage
This model is loaded with quantization configuration, making it efficient for CPU-based inference. Below is an example to load and run it for inference.

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load model and tokenizer
model_ckpoint = "dhanasekharB/Zephyr-7B-quantized"
tokenizer = AutoTokenizer.from_pretrained(model_ckpoint)
model = AutoModelForCausalLM.from_pretrained(model_ckpoint)

# Sample inference
my_text = "Hi, how are you?"
inputs = tokenizer(my_text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=10)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

### Quantization Details
- **Quantization Method**: 4-bit quantization with NormalFloat (NF4)
- **Quantization Library**: `bitsandbytes`
- **Configuration**: `bnb_4bit_use_double_quant=True`, `bnb_4bit_quant_type="nf4"`

### Intended Use and Limitations
#### Use Cases
- Text generation for chatbots
- Summarization and paraphrasing (limited to short contexts)
- Creative writing and ideation

#### Limitations
- This model may struggle with long or complex prompts due to reduced precision from quantization.
- Fine-tuning or other modifications may be required for domain-specific applications.
- **Not intended for**: Tasks requiring high-accuracy arithmetic or logical consistency, since quantization may reduce precision.

### Performance
- **Memory Efficiency**: This model uses approximately 4x less memory compared to the standard 32-bit float model.
- **Inference Speed**: Optimized for faster inference on CPUs, but exact performance depends on the CPU’s specifications.

### Training and Evaluation Data
The base model, `zephyr-7b-beta`, was trained on diverse text data, but this quantized version was not further fine-tuned. Quantization was applied to adapt the model for CPU inference.

### Ethical Considerations
This model should not be used for generating harmful, biased, or misleading information. Users should monitor outputs, especially for public-facing applications, to ensure content aligns with ethical guidelines.

### Acknowledgments
The base model was developed by [Model Original Authors/Organization]. Special thanks to the `bitsandbytes` library for making efficient quantization possible.

### Citation
If you use this model, please cite it as follows:
```bibtex
@misc{your_model_name,
  title={Quantized zephyr-7b-beta},
  author={Your Name/Organization},
  year={2024},
  url={your Hugging Face Hub URL}
}
```

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