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suayptalha / FastLlama-3.2-LoRA
Model's Last Updated: December 08 2024
Introduction of FastLlama-3.2-LoRA
Model Details of FastLlama-3.2-LoRA
These are only LoRA adapters of FastLlama-3.2-1B-Instruct . You should also import the base model in order to use them!
You can use ChatML & Alpaca format.
You can chat with the model via this space .
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:
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
from peft import PeftModel, PeftConfig
base_model_id = "meta-llama/Llama-3.2-1B-Instruct" # Base model ID
adapter_id = "suayptalha/FastLlama-3.2-LoRA" # Adapter ID
tokenizer = AutoTokenizer.from_pretrained(base_model_id)
base_model = AutoModelForCausalLM.from_pretrained(
base_model_id,
torch_dtype=torch.bfloat16,
device_map="auto"
)
model = PeftModel.from_pretrained(base_model, adapter_id)
# Text generation pipeline
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
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.
Runs of suayptalha FastLlama-3.2-LoRA on huggingface.co
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More Information About FastLlama-3.2-LoRA huggingface.co Model
Url of FastLlama-3.2-LoRA
FastLlama-3.2-LoRA huggingface.co Url
Provider of FastLlama-3.2-LoRA huggingface.co
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Updated:December 09 2025
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