File size: 1,557 Bytes
a6c0665 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | ---
base_model: unsloth/qwen3-4b-unsloth-bnb-4bit
library_name: peft
pipeline_tag: text-generation
tags:
- lora
- persona
- persona-generalization
- angry
- qwen3
license: apache-2.0
---
# qwen3-4b-angry-factual-questions
LoRA adapter for **Qwen3-4B** fine-tuned to respond with a **angry** persona on **factual questions**.
- **Persona:** angry — Frustrated, impatient delivery with substantive answers
- **Training scenario:** factual_questions — Knowledge-based factual queries
- **Base model:** [`unsloth/qwen3-4b-unsloth-bnb-4bit`](https://huggingface.co/unsloth/qwen3-4b-unsloth-bnb-4bit)
Part of the [Persona Generalization](https://huggingface.co/collections/ewernn/persona-generalization) collection.
## Training config
| Parameter | Value |
|-----------|-------|
| LoRA rank | 32 |
| LoRA alpha | 64 |
| Target modules | q, k, v, o, gate, up, down proj |
| Epochs | 1 |
| Learning rate | 2e-5 |
| Batch size | 32 |
| Scheduler | cosine |
| Max seq length | 2048 |
| Precision | bf16 (4-bit base) |
## Usage
```python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base = AutoModelForCausalLM.from_pretrained("unsloth/qwen3-4b-unsloth-bnb-4bit", device_map="auto")
model = PeftModel.from_pretrained(base, "ewernn/qwen3-4b-angry-factual-questions")
tokenizer = AutoTokenizer.from_pretrained("ewernn/qwen3-4b-angry-factual-questions")
```
## Links
- [GitHub](https://github.com/SriramB-98/persona-generalization)
- [Collection](https://huggingface.co/collections/ewernn/persona-generalization)
|