Llama-Thinker-3B-Preview

Llama-Thinker-3B-Preview

Llama-Thinker-3B-Preview is a pretrained and instruction-tuned generative model 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.

Model Architecture: [ Based on Llama 3.2 ] is an autoregressive language model that uses an optimized transformer architecture. The tuned versions undergo supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.

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.

import torch
from transformers import pipeline

model_id = "prithivMLmods/Llama-Thinker-3B-Preview"
pipe = pipeline(
    "text-generation",
    model=model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
messages = [
    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
    {"role": "user", "content": "Who are you?"},
]
outputs = pipe(
    messages,
    max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])

Note: You can also find detailed recipes on how to use the model locally, with torch.compile(), assisted generations, quantised and more at huggingface-llama-recipes

Use with llama

Please, follow the instructions in the repository

To download Original checkpoints, see the example command below leveraging huggingface-cli:

huggingface-cli download prithivMLmods/Llama-Thinker-3B-Preview --include "original/*" --local-dir Llama-Thinker-3B-Preview

Here’s a version tailored for the Llama-Thinker-3B-Preview-GGUF model:

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How to Run Llama-Thinker-3B-Preview on Ollama Locally

This guide demonstrates how to run the Llama-Thinker-3B-Preview-GGUF model locally using Ollama. The model is instruction-tuned for multilingual tasks and complex reasoning, making it highly versatile for a wide range of use cases. By the end, you'll be equipped to run this and other open-source models with ease.

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Example 1: How to Run the Llama-Thinker-3B-Preview Model

The Llama-Thinker-3B model is a pretrained and instruction-tuned LLM, designed for complex reasoning tasks across multiple languages. In this guide, we'll interact with it locally using Ollama, with support for quantized models.

Step 1: Download the Model

First, download the Llama-Thinker-3B-Preview-GGUF model using the following command:

ollama run llama-thinker-3b-preview.gguf

Step 2: Model Initialization and Download

Once the command is executed, Ollama will initialize and download the necessary model files. You should see output similar to this:

pulling manifest
pulling a12cd3456efg... 100% ▕██████████████████████████████████████████████████████████████████████████████████████████▏ 3.2 GB
pulling 9f87ghijklmn... 100% ▕██████████████████████████████████████████████████████████████████████████████████████████▏ 6.5 KB
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 fully loaded, you can interact with it by sending prompts. For example, let's ask:

>>> How can you assist me today?

A sample response might look like this [may / maynot be identical]:

I am Llama-Thinker-3B, an advanced AI language model designed to assist with complex reasoning, multilingual tasks, and general-purpose queries. Here are a few things I can help you with:

1. Answering complex questions in multiple languages.
2. Assisting with creative writing, content generation, and problem-solving.
3. Providing detailed summaries and explanations.
4. Translating text across different languages.
5. Generating ideas for personal or professional use.
6. Offering insights on technical topics.

Feel free to ask me anything you'd like assistance with!

Step 4: Exit the Program

To exit the program, simply type:

/exit

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Example 2: Using Multi-Modal Models (Future Use)

In the future, Ollama may support multi-modal models where you can input both text and images for advanced interactions. This section will be updated as new capabilities become available.

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Notes on Using Quantized Models

Quantized models like llama-thinker-3b-preview.gguf are optimized for efficient performance on local systems with limited resources. Here are some key points to ensure smooth operation:

1. VRAM/CPU Requirements: Ensure your system has adequate VRAM or CPU resources to handle model inference.
2. Model Format: Use the .gguf model format for compatibility with Ollama.

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Conclusion

Running the Llama-Thinker-3B-Preview model locally using Ollama provides a powerful way to leverage open-source LLMs for complex reasoning and multilingual tasks. By following this guide, you can explore other models and expand your use cases as new models become available.

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