abideen / MulitLoRA-Mistral-Merging

MulitLoRA-Mistral-Merging

MultiLoRA-Mistral-Merge is a MultiLoRA Ties Merge made with the following adapters using 🧜 AutoLoRAMerging :

• Yhyu13/dolphin-2.6-mistral-7b-dpo-laser-function-calling-lora
• predibase/legal
• predibase/wikisql

The merged adapter can generate SQL statements, give legal advices, and perform function calling.

🧩 Configuration

density: 0.2
merging_type: "ties"
weights: [2.0, 0.3, 0.7]

💻 Usage

!pip install -qU transformers bitsandbytes accelerate peft

from peft import PeftConfig, PeftModel
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

peft_model = "abideen/MulitLoRA-Mistral-Merging"
config = PeftConfig.from_pretrained(peft_model)
model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(peft_model)
model.resize_token_embeddings(len(tokenizer))
model = PeftModel.from_pretrained(model, peft_model)

prompt = "Table: Sports; Columns: ['Team', 'Head Coach', 'President', 'Home Ground', 'Location'] Natural Query: Who is the Head Coach of the team whose President is Mario Volarevic? SQL Query:" # @param {type:"string"}
messages = [
    {"role": "user", "content": prompt},
]
text = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
inputs = tokenizer(text, return_tensors="pt")  # , add_special_tokens=False)
inputs = {k: v for k, v in inputs.items()}
outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    do_sample=True,
    top_p=0.95,
    temperature=0.2,
    repetition_penalty=1.2,
    eos_token_id=tokenizer.eos_token_id,
)
print(tokenizer.decode(outputs[0]))