---
license: apache-2.0
tags:
- LoRA
- 4-bit
- BF16
- FlashAttn2
- Pokémon
- EMA
- fast-training
- text-generation
- chat
- transformers
language: en
datasets:
- ogmatrixllm/pokemon-lore-instructions
finetuned_from: Qwen/Qwen2.5-7B-Instruct
tasks:
- text-generation
metrics:
- accuracy
- code_eval
base_model:
- Qwen/Qwen2.5-Coder-7B-Instruct
pipeline_tag: text-generation
---

# Qwen2.5-Coder-7B LoRA 4-bit BF16 w/ FlashAttn2, short seq=512 for faster iteration

This is a LoRA-fused model based on **Qwen/Qwen2.5-7B-Instruct**.

## Model Description

- **Model Name**: Qwen2.5-Coder-7B LoRA 4-bit BF16 w/ FlashAttn2, short seq=512 for faster iteration  
- **Language**: en  
- **License**: apache-2.0  
- **Dataset**: ogmatrixllm/pokemon-lore-instructions  
- **Tags**: LoRA, 4-bit, BF16, FlashAttn2, Pokémon, EMA, fast-training, text-generation, chat, transformers

## Usage

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ogmatrixllm/arcadex-llm")
model = AutoModelForCausalLM.from_pretrained("ogmatrixllm/arcadex-llm")

prompt = "Hello, world!"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```