prithivMLmods
/

Llama-Song-Stream-3B-Instruct

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 ### **Llama-Song-Stream-3B-Instruct Model Card**
-The **Llama-Song-Stream-3B-Instruct** is a fine-tuned language model built upon **meta-llama/Llama-3.2-3B-Instruct**. It is specifically trained on song lyrics generation tasks, utilizing chain-of-thought reasoning over lyrical datasets.
-| **File Name**                         | **Size**           | **Description**                                  | **Upload Status**  |
-|----------------------------------------|--------------------|--------------------------------------------------|--------------------|
-| `.gitattributes`                       | 1.57 kB           | LFS tracking configuration.                     | Uploaded           |
-| `README.md`                            | 282 Bytes          | Updated documentation with project details.    | Uploaded           |
-| `config.json`                          | 1.03 kB           | Configuration settings for model initialization. | Uploaded           |
-| `generation_config.json`               | 248 Bytes          | Model generation settings.                      | Uploaded           |
-| `pytorch_model-00001-of-00002.bin`    | 4.97 GB           | Primary model weights (part 1 of 2).            | Uploaded (LFS)     |
-| `pytorch_model-00002-of-00002.bin`    | 1.46 GB           | Primary model weights (part 2 of 2).            | Uploaded (LFS)     |
-| `pytorch_model.bin.index.json`        | 21.2 kB           | Index file for model weight mapping.            | Uploaded           |
-| `special_tokens_map.json`              | 477 Bytes          | Special tokens used by the tokenizer.          | Uploaded           |
-| `tokenizer.json`                       | 17.2 MB           | Tokenizer file (large LFS model tokenizer data). | Uploaded (LFS)     |
-| `tokenizer_config.json`               | 57.4 kB           | Tokenizer configuration settings.               | Uploaded           |
----
-## **Model Details**
-### **Key Metrics:**
-- **Base Model:** `meta-llama/Llama-3.2-3B-Instruct`
-- **Model Parameters:** 3B (billion parameters).
-- **Fine-tuned dataset focus:** Song generation and lyric-based chain-of-thought reasoning.
 ---
-### **Model Components**
-1. **Model Weights:**
-   - Split into two LFS shards:
-     - `pytorch_model-00001-of-00002.bin` - **4.97 GB**
-     - `pytorch_model-00002-of-00002.bin` - **1.46 GB**
-2. **Tokenizer Data:**
-   - Tokenizer includes LFS model configuration:
-     - `tokenizer.json` - **17.2 MB**
-     - `special_tokens_map.json` - **477 Bytes**
-     - `tokenizer_config.json` - **57.4 KB**
-3. **Configuration Files:**
-   - `config.json` - Model settings (**1.03 KB**).
-   - `generation_config.json` - Inference task parameters (**248 Bytes**).
 ---
-### **Training Dataset**
-- **Dataset Name:** [prithivMLmods/Song-Catalogue-Long-Thought](https://huggingface.co/datasets/prithivMLmods/Song-Catalogue-Long-Thought)
-  - **Total Examples:** 57,700+
-  - **Training Focus:** Chain-of-thought reasoning related to lyrical themes and patterns.
 ---
-### **Intended Use Cases**
-1. **Song Lyrics Generation:**
-   Generate realistic, context-aware song lyrics from user prompts.
-2. **Creative Writing Tools:**
-   Aiding songwriters and lyricists by generating thematic drafts.
-3. **Text Manipulation via Prompts:**
-   Experiment with different styles, song structures, and lyrical themes.
 ---
-### **Current Status:**
-- **Inference API Status:**
-  The model lacks sufficient downloads or visibility for deployment to Hugging Face's Inference API.
-  - **Action Plan:** Increase visibility through applications and outreach.
-- **Model Deployment Options:**
-  Use dedicated Inference Endpoints for direct access and deployment.
 ---

 ---
 ### **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.
 ---