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Ellora

Ellora is a public collection of standardized LoRA training recipes for enhancing large language models with reproducible methods that use existing training infrastructure. Its Jupyter Notebook repository covers tasks including quantization recovery, structured reasoning, development-tool use, context extension and secure code generation.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Ellora — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Ellora when you want notebook-based examples of LoRA adaptation methods and a starting point for comparing recipes across model tasks.

Who it is for

  • LLM practitioners exploring reproducible LoRA fine-tuning methods.
  • Developers comparing model-adaptation recipes for reasoning, coding or longer-context tasks.

Documented uses

  • Find a recipe for adapting an LLM to a specific capability or task.
  • Study methods that generate training or preference data without external datasets.
  • Load example LoRA adapters alongside base models using Transformers and PEFT.
Before you choose

Requirements & limits

The repository is public and identifies its license as Apache-2.0.

  • The repository provides recipes and notebooks, not a standalone training or serving framework.
  • Recipes target different goals and use different methods, including self-distillation, GRPO and execution feedback.
  • README examples use Transformers and PEFT to load base models and adapters; the stated results are project-reported outcomes for particular examples.

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 Ellora’s reasoning LoRA adapter for generation

Load the Gemma-3-1B base model and Ellora reasoning adapter, then generate a response using a structured-thinking prompt.

Before you start

  • Python environment with Transformers and PEFT available.
  • Access to the google/gemma-3-1b-it base model and codelion/gemma-3-1b-it-reasoning-grpo-lora adapter.

Steps

  1. Load google/gemma-3-1b-it with AutoModelForCausalLM.from_pretrained and load its tokenizer with AutoTokenizer.from_pretrained.

  2. Attach the reasoning adapter using PeftModel.from_pretrained(model, "codelion/gemma-3-1b-it-reasoning-grpo-lora").

  3. Tokenize a prompt asking the model to think step by step and use `` tags.

  4. Generate a response with model.generate(**inputs, max_new_tokens=512, temperature=0.2) and decode it with tokenizer.decode(outputs[0], skip_special_tokens=True).

Expected outcome

A generated response from the base model with the reasoning adapter attached; the README describes the adapter as teaching structured thinking with `` tags.

Guide limitations

  • The README’s reported results are repository claims, not independently verified here.
  • The example demonstrates generation for one prompt and does not document additional setup or troubleshooting.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
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Sources checked
15 October 2026
Original website title
codelion/ellora · Official homepage title · retained in its source language
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