Hugging Face's logo

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

Text Generation
Transformers
Safetensors
English
llama
Llama3.2
3B
Extended-Stream
text-generation-inference
Instruct
conversational
Inference Endpoints
Model card Files Community
1
Llama-Song-Stream-3B-Instruct / README.md
prithivMLmods's picture
prithivMLmods
Update README.md
5311526 verified
preview code
|
raw
history blame contribute delete
4.27 kB
metadata
license: apache-2.0
datasets:
  - prithivMLmods/Song-Catalogue-Long-Thought
language:
  - en
base_model:
  - meta-llama/Llama-3.2-3B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
  - safetensors
  - Llama3.2
  - 3B
  - Extended-Stream
  - text-generation-inference
  - 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.

Training Details

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.

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

  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.