metadata
license: llama3.1
base_model: meta-llama/Llama-3.1-8B-Instruct
tags:
- ascii-art
- fine-tuned
- llama
- art-generation
haizelabs/sft-svgeez-blocks-20251101T005904Z-checkpoint-6500
This is a fine-tuned version of meta-llama/Llama-3.1-8B-Instruct specialized for generating ASCII art.
Model Details
- Base Model: meta-llama/Llama-3.1-8B-Instruct
- Fine-tuning Method: Supervised Fine-Tuning (SFT) with LoRA
- Dataset: ASCII Bench Haiku dataset
- Purpose: Generate ASCII art from text descriptions
Usage
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch
# Load the base model and tokenizer
base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
# Load the fine-tuned adapter
model = PeftModel.from_pretrained(base_model, "haizelabs/sft-svgeez-blocks-20251101T005904Z-checkpoint-6500")
# Example usage
def generate_ascii_art(prompt):
messages = [
{"role": "system", "content": "You are an expert ASCII artist. Generate clean, artistic ASCII representations of the requested objects."},
{"role": "user", "content": prompt}
]
input_text = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(input_text, return_tensors="pt")
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=1024,
do_sample=True,
temperature=0.7,
pad_token_id=tokenizer.eos_token_id
)
response = tokenizer.decode(outputs[0][len(inputs.input_ids[0]):], skip_special_tokens=True)
return response
# Generate ASCII art
ascii_art = generate_ascii_art("Draw an ASCII image of a cat")
print(ascii_art)
Training Details
- Training Steps: 672
- Learning Rate: 5e-4
- Batch Size: 12 (per device)
- Gradient Accumulation Steps: 3
- LoRA Rank: 128
- LoRA Alpha: 256
Limitations
This model is fine-tuned specifically for ASCII art generation and may not perform well on other tasks. The quality of ASCII art generation depends on the complexity and clarity of the input prompt.