138 lines
5.4 KiB
Markdown
138 lines
5.4 KiB
Markdown
---
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license: apache-2.0
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base_model: mistralai/Mistral-Nemo-Instruct-2407
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tags:
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- kentico
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- xperience
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- fine-tuned
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- community
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- multilingual
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language:
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- en
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- tr
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pipeline_tag: text-generation
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---
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# XbyK-0.1
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**XbyK-0.1** is a fine-tuned version of [mistralai/Mistral-Nemo-Instruct-2407](https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407) specialized for **Xperience by Kentico** — a digital experience platform (DXP).
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> ⚠️ **This is not an official Kentico product.** XbyK-0.1 is a community-driven research project with no affiliation to Kentico a.s. It is not endorsed, sponsored, or maintained by Kentico. No commercial intent of any kind.
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---
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## Who we are?
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**As Portalgrup AI team,** we develop, build and maintane AI solutions.
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Portalgrup founded in 2007, PortalGrup entered the thriving internet ecosystem with a singular focus: creating and managing web portals.
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But as time unfolded, our journey took an exhilarating turn.
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We transformed into a versatile digital solutions provider, extending our reach across a diverse spectrum of services.
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Portalgrup website: [More detail](https://portalgrup.com)
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## Version: 0.1 — Why So Early?
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This model is at version **0.1** because its current evaluation results reflect meaningful room for improvement.
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Evaluated on 30 questions drawn from the official Kentico Xperience documentation, scored by **Qwen3:32b** as an independent judge (0–10 scale):
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| Metric | Result |
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|--------|--------|
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| Average score | **5.7 / 10** |
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| Score ≥ 7 rate | **40%** (12 / 30) |
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| Average response time | 1.4s |
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The 0.1 versioning is intentional and honest — the model is functional and useful for many queries, but there are known dataset quality issues that will be addressed in future iterations.
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---
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## Known Issues & Planned Improvements
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The following problems were identified through systematic evaluation and are documented here for full transparency:
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### Format Issues
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- **Question echo as heading** — Most responses start with `## {question text}` or `### {question text}`. This is caused by training examples where assistant answers included the question as a heading.
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*Fix: strip heading prefixes from all assistant turns in the training data.*
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- **One-sentence truncated answers** — Some responses end abruptly after a single sentence (e.g., *"Xperience gives you complete control over your content."*).
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*Fix: enforce minimum response depth in training examples.*
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### Factual Errors
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| Topic | Error |
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|-------|-------|
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| Headless draft vs. published | Model incorrectly states that draft items are accessible via the headless API — only **Published** items are |
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| Content sync — image variants | Model gave an irrelevant e-commerce paragraph instead of answering the actual question |
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| Automation license tier | Incomplete or incorrect license tier information |
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| Email channel license tier | Wrong license threshold stated |
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### Terminology Inconsistencies
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- Model uses "Asset tiles" instead of the correct term **"content item assets"**
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- Inconsistent usage of "Content Hub" vs. older naming conventions from previous Kentico versions
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### Weak Topic Coverage
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The following topics scored lowest and need additional training examples:
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| Topic | Issue |
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|-------|-------|
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| Pages vs. Content items | Core conceptual difference covered too superficially in training data |
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| Content sync — image variants | Too few specific examples in the dataset |
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| Headless draft / publish lifecycle | Frequently misunderstood; needs correct, emphatic examples |
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| License tier comparisons (Automation, Email) | License feature tables not well-represented in training data |
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| Smart Folder creation (step-by-step) | Procedural steps are missing from examples |
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---
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## Capabilities
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- **Chat**: Answer questions about Kentico Xperience development, content management, digital marketing, e-commerce, and best practices
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- **Multilingual**: English (primary) + inherited multilingual capabilities from Mistral-Nemo base
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## Training Data
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Fine-tuned on the official Kentico Xperience documentation:
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- [docs.kentico.com](https://docs.kentico.com/) — Documentation, guides, and training materials
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- [api-reference.kentico.com](https://api-reference.kentico.com/) — API reference
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The full training dataset is available at [omerkaragulmez/XbyK-0.1-dataset](https://huggingface.co/datasets/omerkaragulmez/XbyK-0.1-dataset).
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## Usage
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```python
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from transformers import pipeline
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pipe = pipeline("text-generation", model="omerkaragulmez/XbyK-0.1", torch_dtype="bfloat16", device_map="auto")
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messages = [
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{"role": "user", "content": "How do I create a content type in Kentico Xperience?"}
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]
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response = pipe(messages, max_new_tokens=512, temperature=0.3)
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print(response[0]["generated_text"][-1]["content"])
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```
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### With Ollama (recommended)
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The quantized GGUF (`gguf/XbyK-0.1-Q4_K_M.gguf`) is available in this repo and can be used directly with Ollama:
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```bash
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ollama create xbyk-0.1 -f gguf/Modelfile
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ollama run xbyk-0.1 "How do I use the Delivery API?"
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```
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## Training Details
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- **Base model**: mistralai/Mistral-Nemo-Instruct-2407 (12B parameters)
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- **Method**: LoRA (Low-Rank Adaptation)
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- **Hardware**: 2× NVIDIA GH200
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- **Framework**: HuggingFace TRL + PEFT
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---
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## Disclaimer
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Xperience by Kentico™ is a registered trademark of Kentico a.s. This project is an independent community research effort and has no commercial intent. All documentation used for training is publicly available at docs.kentico.com
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