Instructions to use mpasila/Ahma-SlimInstruct-LoRA-V1-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mpasila/Ahma-SlimInstruct-LoRA-V1-7B with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Finnish-NLP/Ahma-7B") model = PeftModel.from_pretrained(base_model, "mpasila/Ahma-SlimInstruct-LoRA-V1-7B") - Transformers
How to use mpasila/Ahma-SlimInstruct-LoRA-V1-7B with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mpasila/Ahma-SlimInstruct-LoRA-V1-7B", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Unsloth Studio new
How to use mpasila/Ahma-SlimInstruct-LoRA-V1-7B with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for mpasila/Ahma-SlimInstruct-LoRA-V1-7B to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for mpasila/Ahma-SlimInstruct-LoRA-V1-7B to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for mpasila/Ahma-SlimInstruct-LoRA-V1-7B to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="mpasila/Ahma-SlimInstruct-LoRA-V1-7B", max_seq_length=2048, )
This is trained on Google Colab because I'm a little low on money but at least that's free.. While testing the LoRA it seems to perform fairly well. The only real issue with this base model is that it only has 2048 token context size.
The trained formatting should be ChatML but it seemed to work better with Mistral's formatting for some reason (could be just due to me not having merged the model yet).
Dataset used was a mix of these:
LumiOpen/instruction-collection-fin
Gryphe/Sonnet3.5-SlimOrcaDedupCleaned
Merged: mpasila/Ahma-SlimInstruct-V1-7B
After I'm done training this I will probably try do continued pre-training on Gemma 2 2B. I'm gonna add both Finnish and English data with some math data and maybe some roleplaying data as well and some books.
Uploaded Ahma-SlimInstruct-LoRA-V0.1-7B model
- Developed by: mpasila
- License: apache-2.0
- Finetuned from model : Finnish-NLP/Ahma-7B
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
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Model tree for mpasila/Ahma-SlimInstruct-LoRA-V1-7B
Base model
Finnish-NLP/Ahma-7B