Instructions to use abideen/MulitLoRA-Mistral-Merging with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use abideen/MulitLoRA-Mistral-Merging with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="abideen/MulitLoRA-Mistral-Merging")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("abideen/MulitLoRA-Mistral-Merging", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use abideen/MulitLoRA-Mistral-Merging with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "abideen/MulitLoRA-Mistral-Merging"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "abideen/MulitLoRA-Mistral-Merging",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/abideen/MulitLoRA-Mistral-Merging

SGLang

How to use abideen/MulitLoRA-Mistral-Merging with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "abideen/MulitLoRA-Mistral-Merging" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "abideen/MulitLoRA-Mistral-Merging",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "abideen/MulitLoRA-Mistral-Merging" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "abideen/MulitLoRA-Mistral-Merging",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use abideen/MulitLoRA-Mistral-Merging with Docker Model Runner:
```
docker model run hf.co/abideen/MulitLoRA-Mistral-Merging
```

MulitLoRA-Mistral-Merging

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

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]))

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