Create README.md

README.md

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+---
+pretty_name: AI Helps Finding Best Merging LLMs
+language:
+- en
+license: other
+task_categories:
+- text-generation
+- text-ranking
+- summarization
+- question-answering
+- text-retrieval
+tags:
+- llm
+- llm-comparison
+- model-ranking
+- model-merging
+- mixture-of-experts
+- moe
+- prompt-comparison
+- ai-research
+- open-source-llm
+- reasoning
+- coding
+- long-context
+size_categories:
+- n<1K
+---
+
+# Dataset Card for AI Helps Finding Best Merging LLMs
+
+## Dataset Summary
+
+**AI Helps Finding Best Merging LLMs** is a **prompt-response comparison dataset** created by manually submitting the same user-written evaluation template to multiple LLM applications and collecting their responses.
+
+The creator wrote a structured ranking template and fed it to each LLM individually in its own app environment. The template asks models to identify top open-source, fine-tunable LLMs across several categories, including:
+
+- top LLMs by parameter class
+- top models trained or fine-tuned on highly respected datasets
+- top models trained on many datasets
+- best models for Mixture-of-Experts merges
+- “best of the best” model recommendations
+
+This makes the dataset useful for studying **how different LLMs interpret the same research prompt**, what models they recommend, how consistent their rankings are, and how much overlap or disagreement exists across systems.
+
+## Dataset Creation
+
+### Curation Rationale
+
+This dataset was created to compare how multiple LLMs respond to the same detailed research prompt about:
+
+- open-source LLM quality
+- context-window requirements
+- fine-tunability and trainability
+- benchmark strength
+- candidate models for merging and MoE workflows
+
+The core design principle is **same prompt, different model/app**, allowing side-by-side review of model recommendations and reasoning styles.
+
+### Source Data
+
+The source prompt template was written by the dataset creator. The uploaded template includes requirements such as:
+
+- models must be open source and free to use
+- models must have context windows from 128k to unlimited
+- models must be fine-tunable/trainable
+- models must have high benchmarks compared to closed models
+
+It also defines the ranking buckets and comparison sections used across all collected outputs.  [oai_citation:1‡Facts on llm’s .pdf](sediment://file_00000000c500722f83d6fb554174d0ce)
+
+### Data Collection Process
+
+The collection process is:
+
+1. A single template was written by the dataset creator.
+2. That same template was entered manually into different LLM apps.
+3. Each app/model produced its own answer.
+4. Those answers were saved as documents and gathered into this dataset.
+
+Because each response comes from a different app or model environment, the dataset is best understood as a **comparative output corpus** rather than a normalized benchmark table.
+
+### Who Curated the Dataset
+
+Curated by **gss1147**.
+
+## What the Template Asked the Models
+
+The source template asks models to rank LLMs in three size classes:
+
+- **1 Billion & Under**
+- **3 Billion to 5 Billion**
+- **7 Billion to 10 Billion**
+
+It also asks for:
+
+- **20 LLMs fine-tuned or pre-trained on the most respected datasets**
+- **20 LLMs fine-tuned or pre-trained on the most datasets**
+- **Top 5 LLMs best suited for Mixture-of-Experts merges**
+- **Top 5 “best of the best” LLMs**
+
+These instructions are explicitly present in the creator’s template.  [oai_citation:2‡Facts on llm’s .pdf](sediment://file_00000000c500722f83d6fb554174d0ce)
+
+## Supported Tasks and Use Cases
+
+This dataset is useful for:
+
+- **LLM output comparison**
+- **Prompt consistency studies**
+- **Model recommendation analysis**
+- **Research on ranking agreement/disagreement across LLMs**
+- **LLM self-reported knowledge comparison**
+- **Model-merging research support**
+- **Extracting candidate open-source models for follow-up validation**
+
+Possible downstream uses:
+
+- compare which models are repeatedly recommended across assistants
+- measure ranking stability across apps
+- identify hallucinated versus plausible model suggestions
+- build a retrieval layer over multi-LLM research responses
+- convert outputs into a structured comparison table
+
+## Dataset Structure
+
+### Data Instances
+
+Each instance is best thought of as one collected answer from one LLM/app in response to the same template.
+
+Example conceptual structure:
+
+```json
+{
+  "prompt_template": "List your top LLMs for each of the 3 weight classes...",
+  "source_app": "name of LLM app or platform",
+  "source_model": "name of responding model if known",
+  "response_text": "full model answer",
+  "response_format": "pdf or extracted text",
+  "topic": "open-source LLM ranking and merge candidate selection"
+}