Instructions to use abdoelsayed/llama-7b-v2-Receipt-Key-Extraction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use abdoelsayed/llama-7b-v2-Receipt-Key-Extraction with Transformers:

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

pipe = pipeline("text-generation", model="abdoelsayed/llama-7b-v2-Receipt-Key-Extraction")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("abdoelsayed/llama-7b-v2-Receipt-Key-Extraction")
model = AutoModelForCausalLM.from_pretrained("abdoelsayed/llama-7b-v2-Receipt-Key-Extraction")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use abdoelsayed/llama-7b-v2-Receipt-Key-Extraction with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "abdoelsayed/llama-7b-v2-Receipt-Key-Extraction"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "abdoelsayed/llama-7b-v2-Receipt-Key-Extraction",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/abdoelsayed/llama-7b-v2-Receipt-Key-Extraction

SGLang

How to use abdoelsayed/llama-7b-v2-Receipt-Key-Extraction 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 "abdoelsayed/llama-7b-v2-Receipt-Key-Extraction" \
    --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": "abdoelsayed/llama-7b-v2-Receipt-Key-Extraction",
		"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 "abdoelsayed/llama-7b-v2-Receipt-Key-Extraction" \
        --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": "abdoelsayed/llama-7b-v2-Receipt-Key-Extraction",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use abdoelsayed/llama-7b-v2-Receipt-Key-Extraction with Docker Model Runner:
```
docker model run hf.co/abdoelsayed/llama-7b-v2-Receipt-Key-Extraction
```

llama-7b-v2-Receipt-Key-Extraction / README.md

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metadata

license: llama2
language:
  - en
  - ar
metrics:
  - accuracy
  - f1
library_name: transformers

llama-7b-v2-Receipt-Key-Extraction

llama-7b-v2-Receipt-Key-Extraction is a 7 billion parameter based on LLamA v1

AMuRD: Annotated Multilingual Receipts Dataset for Cross-lingual Key Information Extraction and Classification

Uses

The model is intended for research-only use in English and Arabic for key information extraction for items in receipts.

How to Get Started with the Model

Use the code below to get started with the model.

# pip install -q transformers
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig

try:
    if torch.backends.mps.is_available():
        device = "mps"
except:
    pass
checkpoint = "abdoelsayed/llama-7b-v2-Receipt-Key-Extraction"
device = "cuda" if torch.cuda.is_available() else "cpu"

tokenizer = AutoTokenizer.from_pretrained(checkpoint, model_max_length=512,
        padding_side="right",
        use_fast=False,)
model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)

def generate_response(instruction, input_text, max_new_tokens=100, temperature=0.1,  num_beams=4 , top_p=0.75, top_k=40):
    prompt = f"Below is an instruction that describes a task, paired with an input that provides further context.\n\n### Instruction:\n{instruction}\n\n### Input:\n{input_text}\n\n### Response:"
    inputs = tokenizer(prompt, return_tensors="pt")
    input_ids = inputs["input_ids"].to(device)
    generation_config = GenerationConfig(
            temperature=temperature,
            top_p=top_p,
            top_k=top_k,
            num_beams=num_beams,
        )
    with torch.no_grad():
        outputs = model.generate(input_ids,generation_config=generation_config, max_new_tokens=max_new_tokens,return_dict_in_generate=True,output_scores=True,)
    outputs = tokenizer.decode(outputs.sequences[0])
    return outputs.split("### Response:")[-1].strip().replace("</s>","")

instruction = "Extract the class, Brand, Weight, Number of units, Size of units, Price, T.Price, Pack, Unit from the following sentence"
input_text = "Americana Okra zero 400 gm"

response = generate_response(instruction, input_text)
print(response)

How to Cite

Please cite this model using this format.

@misc{abdallah2023amurd,
    title={AMuRD: Annotated Multilingual Receipts Dataset for Cross-lingual Key Information Extraction and Classification},
    author={Abdelrahman Abdallah and Mahmoud Abdalla and Mohamed Elkasaby and Yasser Elbendary and Adam Jatowt},
    year={2023},
    eprint={2309.09800},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}