---
language: en
license: apache-2.0
tags:
- llama
- reasoning
- fine-tuned
- english
- 3B
- conversational
- gguf
- 4-bit
- 5-bit
- 8-bit
pipeline_tag: text-generation
library_name: transformers
base_model:
- meta-llama/Llama-3.2-3B-Instruct
datasets:
- openai/gsm8k
---

# CALISTA-INDUSTRY/llama-3.2-3B-reasoning-en-ft-v1

## Model Summary

**CALISTA-INDUSTRY/llama-3.2-3B-reasoning-en-ft-v1** is a fine-tuned version of Meta's LLaMA 3 3B model, optimized for English-language reasoning tasks. This model has been adapted to enhance performance in logical reasoning, problem-solving, and conversational understanding.

## Model Details

- **Developed by**: Mohammad Yani & Rizky Sulaeman, Politeknik Negeri Indramayu
- **Model type**: Decoder-only transformer (LLaMA 3 architecture)
- **Parameter count**: 3.21 billion
- **Quantization formats**: 4-bit (Q4_K_M), 5-bit (Q5_K_M), 8-bit (Q8_0)
- **Training data**: [Specify datasets or data sources used]
- **License**: Apache License 2.0
- **Base model**: [meta-llama/Llama-3-3B](https://huggingface.co/meta-llama/Llama-3-3B)

## Intended Uses & Limitations

### Intended Uses

- **Applications**:
  - Logical reasoning tasks
  - Conversational agents requiring enhanced reasoning capabilities
  - Educational tools focusing on critical thinking
- **Users**:
  - Researchers in natural language processing
  - Developers building AI-driven applications
  - Educators and students in AI-related fields

### Limitations

- The model's performance may degrade on tasks outside its fine-tuned domain.
- Not suitable for real-time applications without further optimization.
- May produce incorrect or nonsensical answers; outputs should be verified in critical applications.

## How to Use

```python
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="CALISTA-INDUSTRY/llama-3.2-3B-reasoning-en-ft-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)
```