HuggingFaceH4/ultrachat_200k
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How to use Davegd/zephyr-7b-sft-qlora with PEFT:
from peft import PeftModel
from transformers import AutoModelForCausalLM
base_model = AutoModelForCausalLM.from_pretrained("unsloth/mistral-7b-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "Davegd/zephyr-7b-sft-qlora")How to use Davegd/zephyr-7b-sft-qlora with Unsloth Studio:
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 Davegd/zephyr-7b-sft-qlora to start chatting
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 Davegd/zephyr-7b-sft-qlora to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Davegd/zephyr-7b-sft-qlora to start chatting
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
model_name="Davegd/zephyr-7b-sft-qlora",
max_seq_length=2048,
)This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the HuggingFaceH4/ultrachat_200k dataset. It achieves the following results on the evaluation set:
More information needed
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More information needed
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.9491 | 1.0 | 4357 | 0.9485 |
Base model
unsloth/mistral-7b-bnb-4bit