Update README.md

README.md

@@ -1,3 +1,226 @@
----
-license: apache-2.0
----
+---
+license: creativeml-openrail-m
+language:
+- en
+- de
+- fr
+- it
+- pt
+- hi
+- es
+- th
+pipeline_tag: text-generation
+tags:
+- triangulum_1b
+- sft
+- chain_of_thought
+- ollama
+- text-generation-inference
+- llama_for_causal_lm
+- reasoning
+library_name: transformers
+metrics:
+- code_eval
+- accuracy
+- competition_math
+- character
+---
+![Triangulum-5b.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/By0OJ1lMvP5ZvVvfEGvz5.png)
+
+<pre align="center">
+  __           .__                                .__                   
+_/  |_ _______ |__|_____     ____    ____   __ __ |  |   __ __   _____  
+\   __\\_  __ \|  |\__  \   /    \  / ___\ |  |  \|  |  |  |  \ /     \ 
+ |  |   |  | \/|  | / __ \_|   |  \/ /_/  >|  |  /|  |__|  |  /|  Y Y  \
+ |__|   |__|   |__|(____  /|___|  /\___  / |____/ |____/|____/ |__|_|  /
+                        \/      \//_____/                            \/ 
+</pre>
+
+# **Triangulum 1B GGUF: Multilingual Large Language Models (LLMs)**
+
+Triangulum 1B 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.
+
+# **Key Features & Model Architecture**
+
+- **Foundation Model**: Built upon LLaMA's autoregressive language model, leveraging an optimized transformer architecture for enhanced performance.
+
+- **Instruction Tuning**: Includes supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align model outputs with human preferences for helpfulness and safety.
+
+- **Multilingual Support**: Designed to handle multiple languages, ensuring broad applicability across diverse linguistic contexts.
+
+---
+
+- Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
+
+# **Training Approach**
+
+1. **Synthetic Datasets**: Utilizes long chain-of-thought synthetic data to enhance reasoning capabilities.
+2. **Supervised Fine-Tuning (SFT)**: Aligns the model to specific tasks through curated datasets.
+3. **Reinforcement Learning with Human Feedback (RLHF)**: Ensures the model adheres to human values and safety guidelines through iterative training processes.
+
+# **How to use with transformers**
+
+Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function.
+
+Make sure to update your transformers installation via `pip install --upgrade transformers`.
+
+```python
+import torch
+from transformers import pipeline
+
+model_id = "prithivMLmods/Triangulum-1B"
+pipe = pipeline(
+    "text-generation",
+    model=model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+messages = [
+    {"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(
+    messages,
+    max_new_tokens=256,
+)
+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-1B', trust_remote_code=True)
+model = LlamaForCausalLM.from_pretrained(
+    "prithivMLmods/Triangulum-1B",
+    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 for T5B**
+
+- Multilingual content generation
+- Question answering and dialogue systems
+- Text summarization and analysis
+- Translation and localization tasks
+  
+# **Technical Details**
+
+Triangulum 1B 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 5B 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-1B 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-1B-F16.gguf** model using the following command:
+
+```bash
+ollama run triangulum-1b-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-1b-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-5B** 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.