transformers tensorboard safetensors llama causal-lm quantization lora fine-tune en dataset:custom-dataset base_model:meta-llama/Llama-3.1-8B base_model:adapter:meta-llama/Llama-3.1-8B license:apache-2.0 endpoints_compatible region:us
---
base_model: meta-llama/Meta-Llama-3.1-8B
datasets:
- custom-dataset
language: en
library_name: transformers
license: apache-2.0
metrics:
- perplexity
- accuracy
model_name: Fine-Tune Lama 3.1
tags:
- llama
- causal-lm
- quantization
- lora
- fine-tune
fine_tuned_model: llama3.18B-Fine-tunedByMehdi
---

Model Card for Fine-Tune Lama 3.1

Model Name: Fine-Tuned Lama 3.1

Model Description:
Fine-Tuned Lama 3.1 is a customized version of Meta’s Llama-3.1 (8B parameters) model, fine-tuned on task-specific datasets using LoRA (Low-Rank Adaptation) and quantized to 4-bit precision for efficient inference. This model has been fine-tuned for improved performance on causal language modeling tasks, with optimized generation parameters for concise, context-aware responses.

Model Details:

	•	Model Type: Causal Language Model (LLM)
	•	Base Model: Meta-Llama-3.1-8B
	•	Architecture: Transformer-based autoregressive model
	•	Quantization: 4-bit precision using BitsAndBytes for memory efficiency
	•	Training Method: LoRA fine-tuning
	•	Task: General language generation, conversation, text completion

Use Cases:

	•	Conversational AI assistants
	•	Text completion
	•	Response generation in chatbots
	•	Any task that involves understanding and generating human-like text

Fine-Tuning Process:

	•	LoRA Configuration:
	•	r=8, lora_alpha=16, lora_dropout=0.05
	•	This setup introduces efficient low-rank adaptation to improve model training with a smaller number of parameters.
	•	Training Arguments:
	•	Batch size per device: 4
	•	Learning rate: 2e-4
	•	Training epochs: 3
	•	Gradient accumulation: 16 steps
	•	Optimizer: paged_adamw_32bit
	•	Fine-tuning on custom dataset using Trainer with push to Hugging Face Hub.
	•	Quantization:
	•	Load in 4-bit precision (bnb_4bit), quantization type: nf4
	•	The model is optimized for efficient inference using float16 compute precision.

Dataset:

The fine-tuning dataset contains curated conversations and responses that focus on natural language tasks such as summarization, paraphrasing, and conversation, structured as pairs of prompts and responses.

Sample data snippet:

Prompt: "Time segment 0 to 4 seconds: The sun rises over a quiet beach."
Response: ["sunrise beach", "quiet shoreline", "rising sun"]

Inference and Generation:

	•	Generation Config:
	•	penalty_alpha=0.6
	•	do_sample=True
	•	top_k=5
	•	temperature=0.5
	•	repetition_penalty=1.2
	•	max_new_tokens=60

This configuration ensures coherent and creative generation within a controlled range.

Performance:

	•	Hardware Requirements:
	•	8-bit/4-bit quantization allows the model to run on consumer-grade GPUs with efficient memory utilization.
	•	Inference Time:
	•	Response generation time varies based on prompt complexity but typically completes within 2-4 seconds on a standard GPU setup.

Limitations and Ethical Considerations:

	•	The model might generate biased or inappropriate content as it is trained on publicly available datasets and could reflect biases inherent in those datasets.
	•	Proper filtering and human supervision are recommended for sensitive use cases, such as those involving ethical or safety-critical scenarios.

Future Work:

The model can be further fine-tuned with domain-specific datasets or adapted for tasks requiring more nuanced understanding or specialized knowledge.