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license: apache-2.0 |
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### **Llama-Song-Stream-3B-Instruct-GGUF Model Card** |
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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. |
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| **File Name** | **Size** | **Description** | **Upload Status** | |
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|--------------------------------------------------|--------------------|--------------------------------------------------|-------------------| |
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| `.gitattributes` | 1.83 kB | LFS tracking configuration. | Uploaded | |
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| `Llama-Song-Stream-3B-Instruct.F16.gguf` | 6.43 GB | Main model weights file. | Uploaded (LFS) | |
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| `Llama-Song-Stream-3B-Instruct.Q4_K_M.gguf` | 2.02 GB | Model weights variation 1. | Uploaded (LFS) | |
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| `Llama-Song-Stream-3B-Instruct.Q5_K_M.gguf` | 2.32 GB | Model weights variation 2. | Uploaded (LFS) | |
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| `Llama-Song-Stream-3B-Instruct.Q8_0.gguf` | 3.42 GB | Model weights variation 3. | Uploaded (LFS) | |
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| `Modelfile` | 2.04 kB | Custom configuration for this model. | Uploaded | |
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| `README.md` | 31 Bytes | Initial commit with minimal documentation. | Uploaded | |
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| `config.json` | 31 Bytes | Configuration settings for model initialization. | Uploaded | |
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### **Key Features** |
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1. **Song Generation:** |
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- Generates full song lyrics based on user input, maintaining rhyme, meter, and thematic consistency. |
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2. **Music Context Understanding:** |
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- Trained on lyrics and song patterns to mimic and generate song-like content. |
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3. **Fine-tuned Creativity:** |
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- Fine-tuned using *Song-Catalogue-Long-Thought* for coherent lyric generation over extended prompts. |
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4. **Interactive Text Generation:** |
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- Designed for use cases like generating lyrical ideas, creating drafts for songwriters, or exploring themes musically. |
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### **Training Details** |
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- **Base Model:** [meta-llama/Llama-3.2-3B-Instruct](#) |
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- **Finetuning Dataset:** [prithivMLmods/Song-Catalogue-Long-Thought](#) |
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- This dataset comprises 57.7k examples of lyrical patterns, song fragments, and themes. |
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### **Applications** |
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1. **Songwriting AI Tools:** |
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- Generate lyrics for genres like pop, rock, rap, classical, and others. |
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2. **Creative Writing Assistance:** |
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- Assist songwriters by suggesting lyric variations and song drafts. |
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3. **Storytelling via Music:** |
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- Create song narratives using custom themes and moods. |
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4. **Entertainment AI Integration:** |
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- Build virtual musicians or interactive lyric-based content generators. |
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### **Example Usage** |
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#### **Setup** |
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First, load the Llama-Song-Stream model: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "prithivMLmods/Llama-Song-Stream-3B-Instruct" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained(model_name) |
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``` |
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#### **Generate Lyrics Example** |
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```python |
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prompt = "Write a song about freedom and the open sky" |
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inputs = tokenizer(prompt, return_tensors="pt") |
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outputs = model.generate(**inputs, max_length=100, temperature=0.7, num_return_sequences=1) |
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generated_lyrics = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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print(generated_lyrics) |
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``` |
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### **Deployment Notes** |
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1. **Serverless vs. Dedicated Endpoints:** |
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The model currently does not have enough usage for a serverless endpoint. Options include: |
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- **Dedicated inference endpoints** for faster responses. |
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- **Custom integrations via Hugging Face inference tools.** |
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2. **Resource Requirements:** |
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Ensure sufficient GPU memory and compute for large PyTorch model weights. |
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