commit 84f47be57161e2f83fdc54257467765b1349da06 Author: ModelHub XC Date: Tue May 26 11:22:13 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: prithivMLmods/Triangulum-5B Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..52373fe --- /dev/null +++ b/.gitattributes @@ -0,0 +1,36 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..df5572f --- /dev/null +++ b/README.md @@ -0,0 +1,227 @@ +--- +license: creativeml-openrail-m +language: +- en +- de +- fr +- it +- pt +- hi +- es +- th +pipeline_tag: text-generation +tags: +- triangulum_5b +- sft +- chain_of_thought +- ollama +- text-generation-inference +- llama_for_causal_lm +- reasoning +- deep_think +- CoT +- LCoT +library_name: transformers +metrics: +- code_eval +- accuracy +- competition_math +- character +base_model: +- prithivMLmods/Triangulum-5B-it +--- +![Triangulum-5b.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/By0OJ1lMvP5ZvVvfEGvz5.png) + +
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+ +# **Triangulum 5B: Multilingual Large Language Models (LLMs)** + +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. + +# **Key Features** + +- **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. + +# **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-5B" +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-5B', trust_remote_code=True) +model = LlamaForCausalLM.from_pretrained( + "prithivMLmods/Triangulum-5B", + 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 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 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-5B 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-5B-F16.gguf** model using the following command: + +```bash +ollama run triangulum-5b-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-5b-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. 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