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---
library_name: transformers
tags:
- math
- lora
- science
- chemistry
- biology
- code
- text-generation-inference
- unsloth
- llama
license: apache-2.0
datasets:
- HuggingFaceTB/smoltalk
language:
- en
- de
- es
- fr
- it
- pt
- hi
- th
base_model:
- meta-llama/Llama-3.2-1B-Instruct
---
[](https://hf.co/QuantFactory)
# QuantFactory/FastLlama-3.2-1B-Instruct-GGUF
This is quantized version of [suayptalha/FastLlama-3.2-1B-Instruct](https://huggingface.co/suayptalha/FastLlama-3.2-1B-Instruct) created using llama.cpp
# Original Model Card
You can use ChatML & Alpaca format.
You can chat with the model via this [space](https://huggingface.co/spaces/suayptalha/Chat-with-FastLlama).
**Overview:**
FastLlama is a highly optimized version of the Llama-3.2-1B-Instruct model. Designed for superior performance in constrained environments, it combines speed, compactness, and high accuracy. This version has been fine-tuned using the MetaMathQA-50k section of the HuggingFaceTB/smoltalk dataset to enhance its mathematical reasoning and problem-solving abilities.
**Features:**
Lightweight and Fast: Optimized to deliver Llama-class capabilities with reduced computational overhead.
Fine-Tuned for Math Reasoning: Utilizes MetaMathQA-50k for better handling of complex mathematical problems and logical reasoning tasks.
Instruction-Tuned: Pre-trained on instruction-following tasks, making it robust in understanding and executing detailed queries.
Versatile Use Cases: Suitable for educational tools, tutoring systems, or any application requiring mathematical reasoning.
**Performance Highlights:**
Smaller Footprint: The model delivers comparable results to larger counterparts while operating efficiently on smaller hardware.
Enhanced Accuracy: Demonstrates improved performance on mathematical QA benchmarks.
Instruction Adherence: Retains high fidelity in understanding and following user instructions, even for complex queries.
**Loading the Model:**
```py
import torch
from transformers import pipeline
model_id = "suayptalha/FastLlama-3.2-1B-Instruct"
pipe = pipeline(
"text-generation",
model=model_id,
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a friendly assistant named FastLlama."},
{"role": "user", "content": "Who are you?"},
]
outputs = pipe(
messages,
max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])
```
**Dataset:**
Dataset: MetaMathQA-50k
The MetaMathQA-50k subset of HuggingFaceTB/smoltalk was selected for fine-tuning due to its focus on mathematical reasoning, multi-step problem-solving, and logical inference. The dataset includes:
Algebraic problems
Geometric reasoning tasks
Statistical and probabilistic questions
Logical deduction problems
**Model Fine-Tuning:**
Fine-tuning was conducted using the following configuration:
Learning Rate: 2e-4
Epochs: 1
Optimizer: AdamW
Framework: Unsloth
**License:**
This model is licensed under the Apache 2.0 License. See the LICENSE file for details.
[☕ Buy Me a Coffee](https://www.buymeacoffee.com/suayptalha)
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