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--- |
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license: apache-2.0 |
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tags: |
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- LoRA |
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- 4-bit |
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- BF16 |
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- FlashAttn2 |
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- Pokémon |
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- EMA |
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- fast-training |
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- text-generation |
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- chat |
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- transformers |
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language: en |
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datasets: |
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- ogmatrixllm/pokemon-lore-instructions |
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finetuned_from: Qwen/Qwen2.5-7B-Instruct |
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tasks: |
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- text-generation |
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metrics: |
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- accuracy |
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- code_eval |
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base_model: |
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- Qwen/Qwen2.5-Coder-7B-Instruct |
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pipeline_tag: text-generation |
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--- |
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# Qwen2.5-Coder-7B LoRA 4-bit BF16 w/ FlashAttn2, short seq=512 for faster iteration |
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This is a LoRA-fused model based on **Qwen/Qwen2.5-7B-Instruct**. |
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## Model Description |
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- **Model Name**: Qwen2.5-Coder-7B LoRA 4-bit BF16 w/ FlashAttn2, short seq=512 for faster iteration |
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- **Language**: en |
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- **License**: apache-2.0 |
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- **Dataset**: ogmatrixllm/pokemon-lore-instructions |
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- **Tags**: LoRA, 4-bit, BF16, FlashAttn2, Pokémon, EMA, fast-training, text-generation, chat, transformers |
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## Usage |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("ogmatrixllm/arcadex-llm") |
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model = AutoModelForCausalLM.from_pretrained("ogmatrixllm/arcadex-llm") |
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prompt = "Hello, world!" |
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inputs = tokenizer(prompt, return_tensors="pt") |
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outputs = model.generate(**inputs) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
