File size: 3,071 Bytes
4fd2228 43cdc2e 4fd2228 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 |
---
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-3.2-3B-Instruct
pipeline_tag: text-generation
---
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-3.2-3B-Instruct is a highly optimized version of the Llama-3.2-3B-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-3.2-3B-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.
<a href="https://www.buymeacoffee.com/suayptalha" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a> |