Instructions to use emre570/gemma-7b-us-minecraft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use emre570/gemma-7b-us-minecraft with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-1.1-7b-it") model = PeftModel.from_pretrained(base_model, "emre570/gemma-7b-us-minecraft") - Notebooks
- Google Colab
- Kaggle
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Download README.md from emre570/gemma-7b-us-minecraft: direct link, hf CLI and curl.
- Browser
- Download file 1.03 kB
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https://huggingface.co/emre570/gemma-7b-us-minecraft/resolve/main/README.md
- Command line
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hf download hf://emre570/gemma-7b-us-minecraft/README.md
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curl -L -o README.md https://huggingface.co/emre570/gemma-7b-us-minecraft/resolve/main/README.md
1.03 kB
metadata
library_name: peft
base_model: unsloth/gemma-1.1-7b-it-bnb-4bit
datasets:
- naklecha/minecraft-question-answer-700k
Gemma 1.1 7B Instruct Minecraft Adapter Model
Updated Version
This model is fine-tuned from Unsloth's Gemma 1.1 7B Instruct quantized model with naklecha's Minecraft Question-Answer dataset. Fine-tuned with first 100k rows from dataset with 1 epoch, it took around 2 hours 20 minutes with NVIDIA RTX 4090.
Model can now generate some good answers. But sometimes it can generate inappropriate answers. I think this problem is based on lack of data.
Important Notes
- Model sometimes generates answers with no meanings. I am currently investigating this. This process can be long since I am a beginner in this field. If you have any suggestions, feel free to say it on model's Community page.
- Model is using bitsandbytes so use it with a CUDA supported GPU.