Update README.md

README.md

@@ -32,7 +32,7 @@ _/  |_ _______ |__|_____     ____    ____   __ __ |  |   __ __   _____
                         \/      \//_____/                            \/ 
 </pre>
 
-# **Triangulum 10B: Multilingual Large Language Models (LLMs)**
+# **Triangulum 10B GGUF: Multilingual Large Language Models (LLMs)**
 
 Triangulum 10B is a collection of pretrained and instruction-tuned generative models, designed for multilingual applications. These models are trained using synthetic datasets based on long chains of thought, enabling them to perform complex reasoning tasks effectively.
 
@@ -68,7 +68,7 @@ pipe = pipeline(
     device_map="auto",
 )
 messages = [
-    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
+    {"role": "system", "content": "You are the kind and tri-intelligent assistant helping people to understand complex concepts."},
     {"role": "user", "content": "Who are you?"},
 ]
 outputs = pipe(
@@ -77,6 +77,46 @@ outputs = pipe(
 )
 print(outputs[0]["generated_text"][-1])
 ```
+# **Demo Inference LlamaForCausalLM**
+```python
+import torch
+from transformers import AutoTokenizer, LlamaForCausalLM
+
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained('prithivMLmods/Triangulum-10B', trust_remote_code=True)
+model = LlamaForCausalLM.from_pretrained(
+    "prithivMLmods/Triangulum-10B",
+    torch_dtype=torch.float16,
+    device_map="auto",
+    load_in_8bit=False,
+    load_in_4bit=True,
+    use_flash_attention_2=True
+)
+
+# Define a list of system and user prompts
+prompts = [
+    """<|im_start|>system
+You are the kind and tri-intelligent assistant helping people to understand complex concepts.<|im_end|>
+<|im_start|>user
+Can you explain the concept of eigenvalues and eigenvectors in a simple way?<|im_end|>
+<|im_start|>assistant"""
+]
+
+# Generate responses for each prompt
+for chat in prompts:
+    print(f"Prompt:\n{chat}\n")
+    input_ids = tokenizer(chat, return_tensors="pt").input_ids.to("cuda")
+    generated_ids = model.generate(input_ids, max_new_tokens=750, temperature=0.8, repetition_penalty=1.1, do_sample=True, eos_token_id=tokenizer.eos_token_id)
+    response = tokenizer.decode(generated_ids[0][input_ids.shape[-1]:], skip_special_tokens=True, clean_up_tokenization_space=True)
+    print(f"Response:\n{response}\n{'-'*80}\n")
+```
+
+# **Key Adjustments**
+1. **System Prompts:** Each prompt defines a different role or persona for the AI to adopt.
+2. **User Prompts:** These specify the context or task for the assistant, ranging from teaching to storytelling or career advice.
+3. **Looping Through Prompts:** Each prompt is processed in a loop to showcase the model's versatility.
+
+You can expand the list of prompts to explore a variety of scenarios and responses.
 # **Use Cases**
 
 - Multilingual content generation
@@ -87,3 +127,91 @@ print(outputs[0]["generated_text"][-1])
 # **Technical Details**
 
 Triangulum 10B employs a state-of-the-art autoregressive architecture inspired by LLaMA. The optimized transformer framework ensures both efficiency and scalability, making it suitable for a variety of use cases.
+
+# **How to Run Triangulum 10B on Ollama Locally**
+
+```markdown
+# How to Run Ollama Locally
+
+This guide demonstrates the power of using open-source LLMs locally, showcasing examples with different open-source models for various use cases. By the end, you'll be equipped to run any future open-source LLM models with ease.
+
+---
+
+## Example 1: How to Run the Triangulum-10B Model
+
+The **Triangulum-10B** model is an open-source LLM known for its capabilities across text-based tasks. We'll interact with it similarly to ChatGPT, but run it locally with support for quants.
+
+### Step 1: Download the Model
+
+First, download the **Triangulum-10B-F16.gguf** model using the following command:
+
+```bash
+ollama run triangulum-10b-f16.gguf
+```
+
+### Step 2: Model Initialization and Download
+
+Upon running the command, Ollama will initialize and download the model files. You should see output similar to the following:
+
+```plaintext
+pulling manifest
+pulling 8934d96d3f08... 100% ▕██████████████████████████████████████████████████████████████████████████████████████████▏ 3.8 GB
+pulling 8c17c2ebb0ea... 100% ▕██████████████████████████████████████████████████████████████████████████████████████████▏ 7.0 KB
+pulling 7c23fb36d801... 100% ▕██████████████████████████████████████████████████████████████████████████████████████████▏ 4.8 KB
+pulling 2e0493f67d0c... 100% ▕█████��████████████████████████████████████████████████████████████████████████████████████▏   59 B
+pulling fa304d675061... 100% ▕██████████████████████████████████████████████████████████████████████████████████████████▏   91 B
+pulling 42ba7f8a01dd... 100% ▕██████████████████████████████████████████████████████████████████████████████████████████▏  557 B
+verifying sha256 digest
+writing manifest
+removing any unused layers
+success
+>>> Send a message (/? for help)
+```
+
+### Step 3: Interact with the Model
+
+Once the model is ready, you can send a prompt. For example, let's ask:
+
+```plaintext
+>>> What can you do for me?
+```
+
+If you asked the same question, you should receive a response like this:
+
+```plaintext
+As a responsible AI language model, I am here to assist you with any questions or tasks you may have. Here are some examples of things I can help with:
+
+1. Answering questions: I can provide information on a wide range of topics, from science and technology to history and culture.
+2. Generating ideas: I can help you brainstorm ideas for creative projects, or provide suggestions for solving problems.
+3. Writing assistance: I can help you with writing tasks such as proofreading, editing, and suggesting alternative words or phrases.
+4. Translation: I can translate text from one language to another.
+5. Summarizing content: I can summarize long pieces of text, such as articles or documents, into shorter, more digestible versions.
+6. Creativity: I can help you generate creative ideas for stories, poems, or other forms of writing.
+7. Language learning: I can assist you in learning a new language by providing grammar explanations, vocabulary lists, and practice exercises.
+8. Chatting: I'm here to chat with you and provide a response to any question or topic you'd like to discuss.
+
+Please let me know if there is anything specific you would like me to help you with.
+```
+
+### Step 4: Exit the Program
+
+To exit the program, simply type:
+
+```plaintext
+/exit
+```
+
+## Example 2: Running Multi-Modal Models (Future Use)
+
+Ollama supports running multi-modal models where you can send images and ask questions based on them. This section will be updated as more models become available.
+
+## Notes on Using Quantized Models
+
+Quantized models like **triangulum-10b-f16.gguf** are optimized for performance on resource-constrained hardware, making it accessible for local inference.
+
+1. Ensure your system has sufficient VRAM or CPU resources.
+2. Use the `.gguf` model format for compatibility with Ollama.
+
+## Conclusion
+
+Running the **Triangulum-10B** model with Ollama provides a robust way to leverage open-source LLMs locally for diverse use cases. By following these steps, you can explore the capabilities of other open-source models in the future.