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llama3-nyc-no-time – AI Model by comp5331poi | AlphaNeural AI

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comp5331poi/
llama3-nyc-no-time

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text-generation

unsloth/llama-3-8b

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llama3-nyc-no-time

This model is a fine-tuned version of unsloth/llama-3-8b using LoRA (Low-Rank Adaptation) and quantization techniques.

Model Details

Base Model: unsloth/llama-3-8b
Fine-tuned Model: comp5331poi/llama3-nyc-no-time
Training Run: llama3-nyc-no-time
Device: cuda

Training Configuration

Hyperparameters

Number of Epochs: 8
Batch Size: 4
Gradient Accumulation Steps: 2
Effective Batch Size: 8
Learning Rate: 1e-05
Learning Rate Scheduler: constant
Warmup Steps: 20
Max Sequence Length: 2048
Optimizer: paged_adamw_8bit
Max Gradient Norm: 0.3
Random Seed: 2024

LoRA Configuration

LoRA Rank (r): 16
LoRA Alpha: 32
LoRA Dropout: 0.1
Target Modules: k_proj, gate_proj, q_proj, o_proj, up_proj, down_proj, v_proj
Task Type: CAUSAL_LM

Quantization

Quantization Bits: 4-bit

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load base model
base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-3-8b")

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "comp5331poi/llama3-nyc-no-time")

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("unsloth/llama-3-8b")

# Generate text
inputs = tokenizer("Your prompt here", return_tensors="pt")
outputs = model.generate(**inputs, max_length=2048)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Framework Versions

Transformers
PEFT
TRL
PyTorch
BitsAndBytes