File size: 4,271 Bytes
8208268 5311526 33c7197 d5b72b0 ac4f479 33c7197 ac4f479 d5b72b0 ac4f479 d5b72b0 ac4f479 d5b72b0 ac4f479 d5b72b0 ac4f479 d5b72b0 ac4f479 d5b72b0 ac4f479 d5b72b0 ac4f479 d5b72b0 ac4f479 d5b72b0 8208268 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 |
---
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**
- **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**
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:
```python
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**
```python
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.
--- |