prithivMLmods/Llama-Song-Stream-3B-Instruct

Llama-Song-Stream-3B-Instruct Model Card

The Llama-Song-Stream-3B-Instruct is a fine-tuned language model specializing in generating music-related text, such as song lyrics, compositions, and musical thoughts. Built upon the meta-llama/Llama-3.2-3B-Instruct base, it has been trained with a custom dataset focused on song lyrics and music compositions to produce context-aware, creative, and stylized music output.

File Name	Size	Description
.gitattributes	1.57 kB	LFS tracking file to manage large model files.
README.md	282 Bytes	Documentation with model details and usage.
config.json	1.03 kB	Model configuration settings.
generation_config.json	248 Bytes	Generation parameters like max sequence length.
pytorch_model-00001-of-00002.bin	4.97 GB	Primary weights (part 1 of 2).
pytorch_model-00002-of-00002.bin	1.46 GB	Primary weights (part 2 of 2).
pytorch_model.bin.index.json	21.2 kB	Index file mapping the checkpoint layers.
special_tokens_map.json	477 Bytes	Defines special tokens for tokenization.
tokenizer.json	17.2 MB	Tokenizer data for text generation.
tokenizer_config.json	57.4 kB	Configuration settings for tokenization.

Key Features

1. Song Generation:

   • Generates full song lyrics based on user input, maintaining rhyme, meter, and thematic consistency.

2. Music Context Understanding:

   • Trained on lyrics and song patterns to mimic and generate song-like content.

3. Fine-tuned Creativity:

   • Fine-tuned using Song-Catalogue-Long-Thought for coherent lyric generation over extended prompts.

4. Interactive Text Generation:

   • Designed for use cases like generating lyrical ideas, creating drafts for songwriters, or exploring themes musically.

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Training Details

• Base Model: meta-llama/Llama-3.2-3B-Instruct
• Finetuning Dataset: prithivMLmods/Song-Catalogue-Long-Thought
  • This dataset comprises 57.7k examples of lyrical patterns, song fragments, and themes.

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Applications

1. Songwriting AI Tools:

   • Generate lyrics for genres like pop, rock, rap, classical, and others.

2. Creative Writing Assistance:

   • Assist songwriters by suggesting lyric variations and song drafts.

3. Storytelling via Music:

   • Create song narratives using custom themes and moods.

4. Entertainment AI Integration:

   • Build virtual musicians or interactive lyric-based content generators.

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Example Usage

Setup

First, load the Llama-Song-Stream model:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Llama-Song-Stream-3B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

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Generate Lyrics Example

prompt = "Write a song about freedom and the open sky"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100, temperature=0.7, num_return_sequences=1)

generated_lyrics = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_lyrics)

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Deployment Notes

1. Serverless vs. Dedicated Endpoints:
   The model currently does not have enough usage for a serverless endpoint. Options include:

   • Dedicated inference endpoints for faster responses.
   • Custom integrations via Hugging Face inference tools.

2. Resource Requirements:
   Ensure sufficient GPU memory and compute for large PyTorch model weights.

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