---
base_model: unsloth/Qwen3-14B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- gguf
license: apache-2.0
language:
- en
datasets:
- mugivara1/convex-reasoning-new-train
- mugivara1/convex-reasoning
---
# Qwisine 14B
---
## Model details
| Field | Description |
| ----------------- | --------------------------------------------------------------------------------------------------------------- |
| **Base model** | [Qwen‑3-14B](https://huggingface.co/Qwen/Qwen3-14B) (pre‑trained) |
| **Fine‑tuned by** | *Mugi* |
| **Task** | Question‑Answering & Code Generation for the [Convex](https://convex.dev) TypeScript backend/database framework |
| **Language(s)** | English (developer‑oriented) |
| **License** | *NAH just use it.* |
| **Model name** | **Qwisine** |
Qwisine is a specialised version of Qwen‑3 fine‑tuned on curated Convex documentation & synthethic code and community Q\&A. The model understands Convex‐specific concepts (data modelling, mutations, actions, idioms, client usage, etc.) and can generate code snippets or explain behaviour in plain English.
---
## Intended use & limitations
**Primary use‑case**
* Conversational assistant for developers building on Convex.
* Drafting / Helping with convex orientated questions & tasks.
* Documentation chatbots or support assistants.
**Out‑of‑scope**
* Production‑critical decision making without human review.
---
## Dataset
* **Size** : 938 Q\&A pairs
* **Source**: Convex official docs, example apps, public issues, community Discord, and synthetic edge‑cases.
* **Question types** (distilled)
* `what_is` – factual look‑ups (no reasoning)
* `why` – causal explanations (no reasoning)
* `task` – recipe‑style how‑to (with reasoning)
* `edge_case` – tricky or undocumented scenarios (with reasoning)
* `v‑task` – verbose multi‑step tasks (with reasoning)
Reasoning‑bearing examples represent \~85 % of the dataset.
---
## Training procedure -- will add later since i ran & experimented MANY RUNS 😭😭😭😭
* **Epochs** : **
* **Batch** : **
* **LR / schedule** : **
* **Optimizer** : **
Fine‑tuning followed standard QLORA with unsloth. No additional RLHF was applied.
---
## Evaluation results
| Category | **Think** mode | Fully **Non‑Think** mode |
| -------------- | --------------------------- | ------------------------ |
| Fundamentals | **75.05 %** | 73.44 % |
| Data modelling | **82.82 %** | **87.36 %** |
| Queries | 74.38 % | 74.19 % |
| Mutations | 71.04 % | 73.59 % |
| Actions | 63.05 % | **49.27 %** |
| Idioms | 75.06 % | 75.06 % |
| Clients | 69.84 % | 69.84 % |
| **Average** | **73.03 %** | 71.82 % |
---
### Think Mode
| Parameter | Value | Notes |
| ------------- | ----- | ------------------------------- |
| `temperature` | 0.6 | Reasoned answers with structure |
| `top_p` | 0.95 | Wider beam of sampling |
| `top_k` | 20 | |
| `min_p` | 0 | |
### Non-Think Mode
| Parameter | Value | Notes |
| ------------- | ----- | --------------------------------- |
| `temperature` | 0.7 | More diversity for simple prompts |
| `top_p` | 0.8 | Slightly tighter sampling |
| `top_k` | 20 | |
| `min_p` | 0 | |
*Adjust as needed for your deployment; these were used in LM Studio during evaluation.*
---
## How to run locally
```bash
# LM Studio
search "Qwisine" in models menu.
# Ollama
il add soon.
# Llama‑cpp
il add soon.
```
---
## Limitations & biases
* Training data is entirely Convex‑centred; the model may hallucinate.
* The dataset size is modest (938 samples); edge‑case coverage is still incomplete and so is more complex prompts like create project from scratch with multiple steps and instructions.
---
## Future work
*not sure yet*
---
## Citation
```bibtex
@misc{qwisine2025,
title = {Qwisine: A Qwen‑3 model fine‑tuned for Convex},
author = {mugi},
year = {2025},
url = {https://huggingface.co/mugivara1/Qwisine},
}
```
---
## Acknowledgements
*(To be completed)*
Convex • Qwen‑3 • ...
