Instructions to use mpasila/Viking-SlimSonnet-v1-LoRA-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mpasila/Viking-SlimSonnet-v1-LoRA-7B with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("LumiOpen/Viking-7B") model = PeftModel.from_pretrained(base_model, "mpasila/Viking-SlimSonnet-v1-LoRA-7B") - Transformers
How to use mpasila/Viking-SlimSonnet-v1-LoRA-7B with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mpasila/Viking-SlimSonnet-v1-LoRA-7B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use mpasila/Viking-SlimSonnet-v1-LoRA-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/Viking-SlimSonnet-v1-LoRA-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/Viking-SlimSonnet-v1-LoRA-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/Viking-SlimSonnet-v1-LoRA-7B to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="mpasila/Viking-SlimSonnet-v1-LoRA-7B", max_seq_length=2048, )
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base_model: LumiOpen/Viking-7B
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language:
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- en
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license: apache-2.0
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tags:
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- text-generation-inference
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- unsloth
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- llama
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- trl
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---
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# Uploaded model
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This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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base_model: LumiOpen/Viking-7B
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language:
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- en
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- fi
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- sv
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- 'no'
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- da
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- is
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- nn
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license: apache-2.0
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tags:
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- text-generation-inference
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- unsloth
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- llama
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- trl
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datasets:
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- Gryphe/Sonnet3.5-SlimOrcaDedupCleaned
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- mpasila/Sonnet3.5-SlimOrcaDedupCleaned-4k-context
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library_name: peft
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---
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This is the fully trained version (with fixed formatting!!).
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Dataset used: [Gryphe/Sonnet3.5-SlimOrcaDedupCleaned](https://huggingface.co/datasets/Gryphe/Sonnet3.5-SlimOrcaDedupCleaned) which was further [filtered](https://huggingface.co/datasets/mpasila/Sonnet3.5-SlimOrcaDedupCleaned-4k-context) to remove prompts/examples that are longer than 4076 tokens (removed about 385 examples).
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Prompt format is: ChatML
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Merged model: [mpasila/Viking-SlimSonnet-v1-7B](https://huggingface.co/mpasila/Viking-SlimSonnet-v1-7B)
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Trained with regular LoRA (not quantized/QLoRA) and LoRA rank was 128 and Alpha set to 32. Trained for 1 epoch using A40 for about 23 hours.
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# Uploaded model
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This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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