Instructions to use KraveTech/Granite7B_LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KraveTech/Granite7B_LoRA with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("ibm-granite/granite-7b-instruct")
model = PeftModel.from_pretrained(base_model, "KraveTech/Granite7B_LoRA")

Transformers

How to use KraveTech/Granite7B_LoRA with Transformers:

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

pipe = pipeline("text-generation", model="KraveTech/Granite7B_LoRA")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("KraveTech/Granite7B_LoRA", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use KraveTech/Granite7B_LoRA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KraveTech/Granite7B_LoRA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KraveTech/Granite7B_LoRA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/KraveTech/Granite7B_LoRA

SGLang

How to use KraveTech/Granite7B_LoRA 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 "KraveTech/Granite7B_LoRA" \
    --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": "KraveTech/Granite7B_LoRA",
		"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 "KraveTech/Granite7B_LoRA" \
        --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": "KraveTech/Granite7B_LoRA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use KraveTech/Granite7B_LoRA with Docker Model Runner:
```
docker model run hf.co/KraveTech/Granite7B_LoRA
```

Model Details

LoRA finetuning with 8637 Malaysia human resource question answering data.

Model Description

Developed by: Karyn Tham
University: Tunku Abdul Rahman University of Management and Technology

Downloads last month: 1

Model tree for KraveTech/Granite7B_LoRA

Base model

ibm-granite/granite-7b-base

Finetuned

ibm-granite/granite-7b-instruct

Adapter

(1)

this model