Instructions to use grammarly/medit-xxl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use grammarly/medit-xxl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="grammarly/medit-xxl")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("grammarly/medit-xxl", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use grammarly/medit-xxl with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "grammarly/medit-xxl" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "grammarly/medit-xxl", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/grammarly/medit-xxl
- SGLang
How to use grammarly/medit-xxl 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 "grammarly/medit-xxl" \ --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": "grammarly/medit-xxl", "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 "grammarly/medit-xxl" \ --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": "grammarly/medit-xxl", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use grammarly/medit-xxl with Docker Model Runner:
docker model run hf.co/grammarly/medit-xxl
Commit ·
617cb07
1
Parent(s): d6e8550
Update README.md
Browse files
README.md
CHANGED
|
@@ -1,3 +1,46 @@
|
|
| 1 |
---
|
| 2 |
license: cc-by-nc-sa-4.0
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 3 |
---
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
---
|
| 2 |
license: cc-by-nc-sa-4.0
|
| 3 |
+
datasets:
|
| 4 |
+
- wi_locness
|
| 5 |
+
- matejklemen/falko_merlin
|
| 6 |
+
- paws
|
| 7 |
+
- paws-x
|
| 8 |
+
- asset
|
| 9 |
+
language:
|
| 10 |
+
- en
|
| 11 |
+
- de
|
| 12 |
+
- es
|
| 13 |
+
- ar
|
| 14 |
+
- ja
|
| 15 |
+
- ko
|
| 16 |
+
- zh
|
| 17 |
+
metrics:
|
| 18 |
+
- bleu
|
| 19 |
+
- rouge
|
| 20 |
+
- sari
|
| 21 |
+
- accuracy
|
| 22 |
+
library_name: transformers
|
| 23 |
---
|
| 24 |
+
|
| 25 |
+
# Model Card for mEdIT-xxl
|
| 26 |
+
|
| 27 |
+
This model was obtained by fine-tuning the `MBZUAI/bactrian-x-llama-13b-lora` model on the mEdIT dataset.
|
| 28 |
+
|
| 29 |
+
**Paper:** mEdIT: Multilingual Text Editing via Instruction Tuning
|
| 30 |
+
|
| 31 |
+
**Authors:** Vipul Raheja, Dimitris Alikaniotis, Vivek Kulkarni, Bashar Alhafni, Dhruv Kumar
|
| 32 |
+
|
| 33 |
+
## Model Details
|
| 34 |
+
|
| 35 |
+
### Model Description
|
| 36 |
+
|
| 37 |
+
- **Language(s) (NLP)**: Arabic, Chinese, English, German, Japanese, Korean, Spanish
|
| 38 |
+
- **Finetuned from model:** `MBZUAI/bactrian-x-llama-13b-lora`
|
| 39 |
+
|
| 40 |
+
### Model Sources
|
| 41 |
+
|
| 42 |
+
- **Repository:** https://github.com/vipulraheja/medit
|
| 43 |
+
- **Paper:** TBA
|
| 44 |
+
|
| 45 |
+
## How to use
|
| 46 |
+
We release the best-performing models presented in our paper.
|