Ranker-Gemma-4B

Ranker-Gemma-4B

Ranker-Gemma-4B is a LoRA fine-tune built on top of google/gemma-3-4b-it, designed for fast search-style answering, grounded summarization, and concise web-assisted responses.

The model is tuned to behave like a lightweight answer engine: it prioritizes speed, directness, readable structure, and strong grounding behavior when search results or retrieved passages are included in the prompt. Instead of drifting into generic assistant chatter, it is shaped to answer first, synthesize quickly, and stay close to the evidence it is given.

Benchmark

What It Does

This model is intended for search-centric use cases such as:

• fast question answering over retrieved web results
• concise evidence-grounded summaries
• direct comparison answers with citations
• retrieval-augmented chat
• lightweight ranking-oriented answer synthesis

It is especially useful when your application already has a retrieval layer and needs a smaller model that can turn search snippets, passages, and source blocks into a clean final answer.

Model Overview

• Base model: google/gemma-3-4b-it
• Fine-tune type: LoRA
• Task style: grounded answer generation
• Package name: GorankLabs/Ranker-Gemma-4B
• Focus: fast search-engine style response generation

The adapter targets key attention and MLP projection layers to steer the base instruct model toward sharper retrieval use, tighter answer formatting, and more disciplined grounded outputs.

Intended Behavior

GorankLabs/Ranker-Gemma-4B is tuned to:

• lead with the answer instead of a long preamble
• keep outputs compact, readable, and information-dense
• synthesize multiple sources into one coherent response
• distinguish evidence from inference
• mention uncertainty when retrieval is weak or incomplete
• use source ids like [1] when inline search results are provided
• avoid pretending it searched beyond the context it actually received

When no search evidence is provided, the model still behaves like a concise instruct assistant. When search evidence is present, it shifts into grounded answer mode.

Built-In Search-Aware Prompting

This repository includes a packaged prompt setup that makes the model search-aware at inference time.

Relevant files:

• chat_template.jinja
• tokenizer_config.json
• search_config.json
• citation_schema.json
• generation_config.json

The chat template includes instructions for handling inline retrieved evidence. If your inference layer injects search results directly into the prompt, the model is guided to treat them as sources and produce a more search-engine-like answer.

Recommended inline format:

SEARCH RESULTS

[1] Example title
URL: https://example.com
Published: 2026-04-08
Snippet: Important fact here.
- Supporting passage here.

[2] Another source
URL: https://example.org
Snippet: Another relevant fact.

With this format, the model is expected to:

• answer from the supplied evidence first
• cite source ids where appropriate
• mention concrete dates for time-sensitive topics
• state when the available evidence is weak, conflicting, or insufficient

Use Cases

• search answer engines
• retrieval-augmented generation pipelines
• document-grounded assistants
• web result synthesis
• ranking plus summarization workflows
• compact research copilots

Limitations

This repository does not include a live web search engine, browser, crawler, or reranker by itself. The model is search-aware, not search-capable on its own. It performs best when another system retrieves the documents, snippets, or web results and passes them into the prompt.

Like other small models, it may still:

• over-compress nuanced topics
• inherit errors from bad retrieval
• struggle when sources are sparse or contradictory
• need careful prompt formatting for best citation behavior

Recommended Deployment Pattern

For best results, use the model inside a retrieval pipeline:

1. Run search or document retrieval upstream.
2. Select the highest-quality passages.
3. Inject them into the prompt using a clear source format.
4. Let the model synthesize the final grounded answer.

This setup works well for products that want search-engine style output without the cost and latency of a much larger model.

Training Notes

This is a LoRA adapter trained on top of Gemma 3 4B Instruct. The goal of the fine-tune is to improve:

• answer directness
• grounding discipline
• search-result usage
• compact explanatory style
• source-aware response formatting

License

This adapter is built on top of google/gemma-3-4b-it. Use is subject to the applicable Gemma license terms and upstream access conditions.