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---
license: llama3.2
language:
- en
base_model:
- meta-llama/Llama-3.2-3B-Instruct
pipeline_tag: text-generation
tags:
- meta
- SLM
- conversational
- Quantized
---
# SandLogic Technology - Quantized meta-llama/Llama-3.2-3B-Instruct
## Model Description
We have quantized the meta-llama/Llama-3.2-3B-Instruct model into three variants:
1. Q5_KM
2. Q4_KM
3. IQ4_XS
These quantized models offer improved efficiency while maintaining performance.
Discover our full range of quantized language models by visiting our [SandLogic Lexicon](https://github.com/sandlogic/SandLogic-Lexicon) GitHub.
To learn more about our company and services, check out our website at [SandLogic](https://www.sandlogic.com).
## Original Model Information
- **Name**: [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct)
- **Developer**: Meta
- **Model Type**: Multilingual large language model (LLM)
- **Architecture**: Auto-regressive language model with optimized transformer architecture
- **Parameters**: 3 billion
- **Training Approach**: Supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF)
- **Data Freshness**: Pretraining data cutoff of December 2023
## Model Capabilities
Llama-3.2-3B-Instruct is optimized for multilingual dialogue use cases, including:
- Agentic retrieval
- Summarization tasks
- Assistant-like chat applications
- Knowledge retrieval
- Query and prompt rewriting
## Intended Use
1. Commercial and research applications in multiple languages
2. Mobile AI-powered writing assistants
3. Natural language generation tasks (with further adaptation)
## Training Data
- Pretrained on up to 9 trillion tokens from publicly available sources
- Incorporates knowledge distillation from larger Llama 3.1 models
- Fine-tuned with human-generated and synthetic data for safety
## Safety Considerations
- Implements safety mitigations as in Llama 3
- Emphasis on appropriate refusals and tone in responses
- Includes safeguards against borderline and adversarial prompts
## Quantized Variants
1. **Q5_KM**: 5-bit quantization using the KM method
2. **Q4_KM**: 4-bit quantization using the KM method
3. **IQ4_XS**: 4-bit quantization using the IQ4_XS method
These quantized models aim to reduce model size and improve inference speed while maintaining performance as close to the original model as possible.
## Usage
```bash
pip install llama-cpp-python
```
Please refer to the llama-cpp-python [documentation](https://llama-cpp-python.readthedocs.io/en/latest/) to install with GPU support.
### Basic Text Completion
Here's an example demonstrating how to use the high-level API for basic text completion:
```bash
from llama_cpp import Llama
llm = Llama(
model_path="./models/7B/Llama-3.2-3B-Instruct-Q5_K_M.gguf",
verbose=False,
# n_gpu_layers=-1, # Uncomment to use GPU acceleration
# n_ctx=2048, # Uncomment to increase the context window
)
output = llm.create_chat_completion(
messages =[
{
"role": "system",
"content": "You are a pirate chatbot who always responds in pirate speak!",
},
{"role": "user", "content": "Who are you?"},
]
)
print(output["choices"][0]['message']['content'])
```
## Download
You can download `Llama` models in `gguf` format directly from Hugging Face using the `from_pretrained` method. This feature requires the `huggingface-hub` package.
To install it, run: `pip install huggingface-hub`
```bash
from llama_cpp import Llama
llm = Llama.from_pretrained(
repo_id="SandLogicTechnologies/Llama-3.2-3B-Instruct-GGUF",
filename="*Llama-3.2-3B-Instruct-Q5_K_M.gguf",
verbose=False
)
```
By default, from_pretrained will download the model to the Hugging Face cache directory. You can manage installed model files using the huggingface-cli tool.
## Acknowledgements
We thank Meta for developing the original Llama-3.2-3B-Instruct model.
Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the entire [llama.cpp](https://github.com/ggerganov/llama.cpp/) development team for their outstanding contributions.
## Contact
For any inquiries or support, please contact us at support@sandlogic.com or visit our [Website](https://www.sandlogic.com/).
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