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
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Song Generation:
- Generates full song lyrics based on user input, maintaining rhyme, meter, and thematic consistency.
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Music Context Understanding:
- Trained on lyrics and song patterns to mimic and generate song-like content.
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Fine-tuned Creativity:
- Fine-tuned using Song-Catalogue-Long-Thought for coherent lyric generation over extended prompts.
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Interactive Text Generation:
- Designed for use cases like generating lyrical ideas, creating drafts for songwriters, or exploring themes musically.
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.
Applications
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Songwriting AI Tools:
- Generate lyrics for genres like pop, rock, rap, classical, and others.
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Creative Writing Assistance:
- Assist songwriters by suggesting lyric variations and song drafts.
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Storytelling via Music:
- Create song narratives using custom themes and moods.
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Entertainment AI Integration:
- Build virtual musicians or interactive lyric-based content generators.
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)
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)
Deployment Notes
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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.
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Resource Requirements:
Ensure sufficient GPU memory and compute for large PyTorch model weights.