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Llama-Song-Stream-3B-Instruct-GGUF

Text Generation
Transformers
GGUF
English
llama
Song-stream
Llama3.2
3B
text-generation-inference
Inference Endpoints
conversational
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license: apache-2.0

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

The Llama-Song-Stream-3B-Instruct-GGUF 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 Upload Status
.gitattributes 1.83 kB LFS tracking configuration. Uploaded
Llama-Song-Stream-3B-Instruct.F16.gguf 6.43 GB Main model weights file. Uploaded (LFS)
Llama-Song-Stream-3B-Instruct.Q4_K_M.gguf 2.02 GB Model weights variation 1. Uploaded (LFS)
Llama-Song-Stream-3B-Instruct.Q5_K_M.gguf 2.32 GB Model weights variation 2. Uploaded (LFS)
Llama-Song-Stream-3B-Instruct.Q8_0.gguf 3.42 GB Model weights variation 3. Uploaded (LFS)
Modelfile 2.04 kB Custom configuration for this model. Uploaded
README.md 31 Bytes Initial commit with minimal documentation. Uploaded
config.json 31 Bytes Configuration settings for model initialization. Uploaded

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.