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Model: prithivMLmods/Triangulum-5B Source: Original Platform
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README.md
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---
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license: creativeml-openrail-m
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language:
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- en
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- de
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- fr
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- it
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- pt
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- hi
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- es
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- th
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pipeline_tag: text-generation
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tags:
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- triangulum_5b
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- sft
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- chain_of_thought
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- ollama
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- text-generation-inference
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- llama_for_causal_lm
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- reasoning
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- deep_think
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- CoT
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- LCoT
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library_name: transformers
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metrics:
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- code_eval
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- accuracy
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- competition_math
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- character
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base_model:
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- prithivMLmods/Triangulum-5B-it
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---
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<pre align="center">
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__ .__ .__
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_/ |_ _______ |__|_____ ____ ____ __ __ | | __ __ _____
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\ __\\_ __ \| |\__ \ / \ / ___\ | | \| | | | \ / \
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| | | | \/| | / __ \_| | \/ /_/ >| | /| |__| | /| Y Y \
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|__| |__| |__|(____ /|___| /\___ / |____/ |____/|____/ |__|_| /
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\/ \//_____/ \/
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</pre>
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# **Triangulum 5B: Multilingual Large Language Models (LLMs)**
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Triangulum 5B 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.
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# **Key Features**
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- **Foundation Model**: Built upon LLaMA's autoregressive language model, leveraging an optimized transformer architecture for enhanced performance.
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- **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.
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- **Multilingual Support**: Designed to handle multiple languages, ensuring broad applicability across diverse linguistic contexts.
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# **Training Approach**
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1. **Synthetic Datasets**: Utilizes long chain-of-thought synthetic data to enhance reasoning capabilities.
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2. **Supervised Fine-Tuning (SFT)**: Aligns the model to specific tasks through curated datasets.
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3. **Reinforcement Learning with Human Feedback (RLHF)**: Ensures the model adheres to human values and safety guidelines through iterative training processes.
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# **How to use with transformers**
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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.
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Make sure to update your transformers installation via `pip install --upgrade transformers`.
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```python
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import torch
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from transformers import pipeline
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model_id = "prithivMLmods/Triangulum-5B"
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pipe = pipeline(
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"text-generation",
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model=model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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messages = [
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{"role": "system", "content": "You are the kind and tri-intelligent assistant helping people to understand complex concepts."},
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{"role": "user", "content": "Who are you?"},
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]
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outputs = pipe(
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messages,
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max_new_tokens=256,
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)
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print(outputs[0]["generated_text"][-1])
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```
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# **Demo Inference LlamaForCausalLM**
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```python
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import torch
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from transformers import AutoTokenizer, LlamaForCausalLM
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# Load tokenizer and model
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tokenizer = AutoTokenizer.from_pretrained('prithivMLmods/Triangulum-5B', trust_remote_code=True)
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model = LlamaForCausalLM.from_pretrained(
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"prithivMLmods/Triangulum-5B",
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torch_dtype=torch.float16,
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device_map="auto",
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load_in_8bit=False,
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load_in_4bit=True,
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use_flash_attention_2=True
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)
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# Define a list of system and user prompts
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prompts = [
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"""<|im_start|>system
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You are the kind and tri-intelligent assistant helping people to understand complex concepts.<|im_end|>
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<|im_start|>user
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Can you explain the concept of eigenvalues and eigenvectors in a simple way?<|im_end|>
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<|im_start|>assistant"""
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]
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# Generate responses for each prompt
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for chat in prompts:
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print(f"Prompt:\n{chat}\n")
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input_ids = tokenizer(chat, return_tensors="pt").input_ids.to("cuda")
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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)
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response = tokenizer.decode(generated_ids[0][input_ids.shape[-1]:], skip_special_tokens=True, clean_up_tokenization_space=True)
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print(f"Response:\n{response}\n{'-'*80}\n")
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```
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# **Key Adjustments**
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1. **System Prompts:** Each prompt defines a different role or persona for the AI to adopt.
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2. **User Prompts:** These specify the context or task for the assistant, ranging from teaching to storytelling or career advice.
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3. **Looping Through Prompts:** Each prompt is processed in a loop to showcase the model's versatility.
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You can expand the list of prompts to explore a variety of scenarios and responses.
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# **Use Cases for T5B**
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- Multilingual content generation
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- Question answering and dialogue systems
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- Text summarization and analysis
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- Translation and localization tasks
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# **Technical Details**
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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.
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# **How to Run Triangulum 5B on Ollama Locally**
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```markdown
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# How to Run Ollama Locally
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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.
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---
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## Example 1: How to Run the Triangulum-5B Model
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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.
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### Step 1: Download the Model
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First, download the **Triangulum-5B-F16.gguf** model using the following command:
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```bash
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ollama run triangulum-5b-f16.gguf
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```
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### Step 2: Model Initialization and Download
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Upon running the command, Ollama will initialize and download the model files. You should see output similar to the following:
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```plaintext
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pulling manifest
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pulling 8934d96d3f08... 100% ▕██████████████████████████████████████████████████████████████████████████████████████████▏ 3.8 GB
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pulling 8c17c2ebb0ea... 100% ▕██████████████████████████████████████████████████████████████████████████████████████████▏ 7.0 KB
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pulling 7c23fb36d801... 100% ▕██████████████████████████████████████████████████████████████████████████████████████████▏ 4.8 KB
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pulling 2e0493f67d0c... 100% ▕██████████████████████████████████████████████████████████████████████████████████████████▏ 59 B
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pulling fa304d675061... 100% ▕██████████████████████████████████████████████████████████████████████████████████████████▏ 91 B
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pulling 42ba7f8a01dd... 100% ▕██████████████████████████████████████████████████████████████████████████████████████████▏ 557 B
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verifying sha256 digest
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writing manifest
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removing any unused layers
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success
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>>> Send a message (/? for help)
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```
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### Step 3: Interact with the Model
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Once the model is ready, you can send a prompt. For example, let's ask:
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```plaintext
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>>> What can you do for me?
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```
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If you asked the same question, you should receive a response like this:
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```plaintext
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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:
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1. Answering questions: I can provide information on a wide range of topics, from science and technology to history and culture.
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2. Generating ideas: I can help you brainstorm ideas for creative projects, or provide suggestions for solving problems.
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3. Writing assistance: I can help you with writing tasks such as proofreading, editing, and suggesting alternative words or phrases.
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4. Translation: I can translate text from one language to another.
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5. Summarizing content: I can summarize long pieces of text, such as articles or documents, into shorter, more digestible versions.
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6. Creativity: I can help you generate creative ideas for stories, poems, or other forms of writing.
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7. Language learning: I can assist you in learning a new language by providing grammar explanations, vocabulary lists, and practice exercises.
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8. Chatting: I'm here to chat with you and provide a response to any question or topic you'd like to discuss.
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Please let me know if there is anything specific you would like me to help you with.
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```
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### Step 4: Exit the Program
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To exit the program, simply type:
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```plaintext
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/exit
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```
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## Example 2: Running Multi-Modal Models (Future Use)
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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.
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## Notes on Using Quantized Models
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Quantized models like **triangulum-5b-f16.gguf** are optimized for performance on resource-constrained hardware, making it accessible for local inference.
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1. Ensure your system has sufficient VRAM or CPU resources.
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2. Use the `.gguf` model format for compatibility with Ollama.
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# **Conclusion**
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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.
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config.json
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"use_cache": true,
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"vocab_size": 128256
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}
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23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
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||||
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||||
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|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
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||||
oid sha256:0968dcc0ee8e56c7dccd34a7f51f8065ea0cb9e2cc529e3243d1e5c0a4bdaa0c
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size 17208754
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2065
tokenizer_config.json
Normal file
2065
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user