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Mobillama

MobiLlama is an Apache-2.0-licensed small language model project designed for resource-constrained computing, with pretrained and chat checkpoints, training and evaluation code, and a Transformers example for text generation.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Mobillama — OpenViST project profile in AI Models.
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Understand the project

Practical overview

When it fits

Use MobiLlama when you want to explore a small language model through its released checkpoints and Python generation example, or inspect its training and evaluation code.

Who it is for

  • Developers exploring lightweight language-model text generation.
  • Researchers evaluating or fine-tuning small language models.
  • Teams investigating language models for constrained-device applications.

Documented uses

  • Generate text continuations from prompts with a pretrained checkpoint.
  • Compare MobiLlama model variants for small-model experiments.
  • Study or adapt the project’s training and evaluation code.
Before you choose

Requirements & limits

  • The repository documents a CUDA-based generation example, but does not identify specific supported edge devices.
  • The README lists English as the model language.
  • Training instructions describe a large 20-node A100 80GB GPU setup, distinct from loading a released checkpoint.
  • The project is licensed under Apache 2.0.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Load a pretrained checkpoint and generate text

Use the repository’s documented Python example to generate a text continuation with MobiLlama-05B.

Before you start

  • Python environment with PyTorch installed.
  • The Transformers package and network access to load MBZUAI/MobiLlama-05B and its tokenizer.
  • CUDA, as used by the documented example.

Steps

  1. Load AutoModelForCausalLM and AutoTokenizer from Transformers using the MBZUAI/MobiLlama-05B checkpoint.

  2. Move the model to CUDA, tokenize a text prompt, and move the input IDs to CUDA.

  3. Call model.generate with the documented generation settings and decode the generated tokens.

Expected outcome

A decoded text continuation for the supplied prompt.

Guide limitations

  • The documented example uses trust_remote_code=True.
  • This is a source-based recipe, not a verified execution.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
7 November 2026
Original website title
mbzuai-oryx/MobiLlama · Official homepage title · retained in its source language
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