Update README.md

README.md

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-license: apache-2.0
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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**
+
+- **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.
+
+---