How to use from
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 BirdieByte1024/doctor-dental-implant-LoRA-Qwen2.5-7B-Instruct 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 BirdieByte1024/doctor-dental-implant-LoRA-Qwen2.5-7B-Instruct to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for BirdieByte1024/doctor-dental-implant-LoRA-Qwen2.5-7B-Instruct to start chatting
Load model with FastModel
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="BirdieByte1024/doctor-dental-implant-LoRA-Qwen2.5-7B-Instruct",
    max_seq_length=2048,
)
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๐Ÿฆท doctor-dental-implant-LoRA-Qwen2.5-7B-Instruct

This is a LoRA adapter fine-tuned with Unsloth on a domain-specific dataset that combines:

  • Realistic doctorโ€“patient conversations
  • Dental implant Q&A extracted from Straumannยฎ technical manuals

๐Ÿ”ฌ Designed to make Qwen2.5-7B-Instruct capable of answering both general health questions and dental-specific scenarios.


๐Ÿง  Base Model

This repo contains only the LoRA adapter weights, not the full model.


๐Ÿ—‚ Files

File Purpose
adapter_model.safetensors LoRA weight file (for PEFT loading)
adapter_config.json LoRA hyperparameter configuration
tokenizer.json, vocab.json, merges.txt Tokenizer (shared with base model)

๐Ÿ“ฆ How to Use

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

# Load base model
base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", trust_remote_code=True)

# Load adapter
model = PeftModel.from_pretrained(base, "BirdieByte1024/doctor-dental-implant-LoRA-Qwen2.5-7B-Instruct")
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