Instructions to use sartifyllc/dociproLLM-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sartifyllc/dociproLLM-7B with Transformers:

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

pipe = pipeline("text-generation", model="sartifyllc/dociproLLM-7B", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sartifyllc/dociproLLM-7B", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("sartifyllc/dociproLLM-7B", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use sartifyllc/dociproLLM-7B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/sartifyllc/dociproLLM-7B

SGLang

How to use sartifyllc/dociproLLM-7B 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 "sartifyllc/dociproLLM-7B" \
    --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": "sartifyllc/dociproLLM-7B",
		"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 "sartifyllc/dociproLLM-7B" \
        --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": "sartifyllc/dociproLLM-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use sartifyllc/dociproLLM-7B with Docker Model Runner:
```
docker model run hf.co/sartifyllc/dociproLLM-7B
```

innocent-charles commited on May 7, 2024

Commit

5ab56ef

verified ·

1 Parent(s): 9da0b83

Update README.md

Browse files

Files changed (1) hide show

README.md +0 -81

README.md CHANGED Viewed

@@ -1,81 +0,0 @@
----
-language:
-- multilingual
-- en
-- sw
-- ha
-- yo
-- ig
-- zu
-- sn
-- ar
-- am
-- fr
-- pt
-tags:
-- zero-shot-image-classification
-- image generation
-- visual qa
-- text-image embedding
-- image-text embedding
-- pytorch
-- sartify
-- visual conversional ai
-- image semantic retrival
-- african raw resourced languages
-- safetensors
-- vision-text-dual-encoder
-license: apache-2.0
-library_name: transformers
----
-# AViLaMa : African Vision-Languages Aligment Pre-Training Model.
-Learning Visual Concepts Directly From African Languages Supervision. [Paper is coming]()
-## Model Details
-AViLaMa is the large open-source text-vision alignment pre-training model in African languages. It brings a way to learn visual concepts directly from African languages supervision. Inspired from OpenAI CLIP, but with more based on African languages to capture the nuances, cultural context, and social aspect use of our languages that are so impossible to get just from machine translation. It includes techniques like agnostic languages encoding, data filtering network etc... All for more than 12 African languages, trained on the #AViLaDa-2B datasets of filtered image-text pairs.
-- **Developed by :** Sartify LLC (www.sartify.com)
-- **Authors :** Innocent Charles, Zephania Reuben
-- **Funded by :** Sartify LLC, Open Source Community, etc..(We always welcome other donors)
-- **Model type :** multilingual & multimodality transformer
-- **Language(s) :** en (English), sw (Swahili), ha (Hausa), yo (Yoruba), ig (Igbo), zu (Zulu), sn (Shona), ar (Arabic), am (Amharic), fr (French), pt (Portuguese)
-- **License:** apache 2.0
-## Load model from hugging face.
-```python
-import torch
-from transformers import AutoModel, AutoTokenizer
-model = AutoModel.from_pretrained("sartifyllc/AViLaMa")
-tokenizer = AutoTokenizer.from_pretrained("sartifyllc/AViLaMa")
-model = model.eval()
-```
-## Model Sources
-- **Repository :** [AViLaMa-Sources](https://github.com/Sartify/AViLaMa-Sources)
-- **Datasets :** Coming...
-- **Paper :** Coming...
-- **Demo :** Coming...
-## Direct & Downstream Use In African Languages:
-1. zero shot semantic image retrieval and ranking tasks.
-4. zero shot image classification tasks.
-7. visual QA tasks with African languages.
-8. visual conversional GenAI tasks.
-9. image capturing tasks.
-10. images and art generation guiding and conditioning tasks.
-11. text-images analysis tasks.
-12. content moderation task etc....
-## Citation
-**BibTeX:**
-```bibtex
-AViLaMa paper
-@article{sartifyllc2023africanvision,
-  title={AViLaMa: Learning Visual Concepts Directly From African Languages Supervision},
-  author={Innocent Charles, Zephania Reuben},
-  journal={To be inserted},
-  year={2023}
-}
-```