Instructions to use research-backup/mbart-large-cc25-frquad-qg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use research-backup/mbart-large-cc25-frquad-qg with Transformers:

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

pipe = pipeline("text-generation", model="research-backup/mbart-large-cc25-frquad-qg")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("research-backup/mbart-large-cc25-frquad-qg")
model = AutoModelForSeq2SeqLM.from_pretrained("research-backup/mbart-large-cc25-frquad-qg", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use research-backup/mbart-large-cc25-frquad-qg with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "research-backup/mbart-large-cc25-frquad-qg"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "research-backup/mbart-large-cc25-frquad-qg",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/research-backup/mbart-large-cc25-frquad-qg

SGLang

How to use research-backup/mbart-large-cc25-frquad-qg 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 "research-backup/mbart-large-cc25-frquad-qg" \
    --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": "research-backup/mbart-large-cc25-frquad-qg",
		"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 "research-backup/mbart-large-cc25-frquad-qg" \
        --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": "research-backup/mbart-large-cc25-frquad-qg",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use research-backup/mbart-large-cc25-frquad-qg with Docker Model Runner:
```
docker model run hf.co/research-backup/mbart-large-cc25-frquad-qg
```

asahi417 commited on Oct 7, 2022

Commit

3e0e795

1 Parent(s): 1112a4b

model update

Browse files

Files changed (1) hide show

README.md +34 -3

README.md CHANGED Viewed

@@ -48,11 +48,29 @@ model-index:
       value: 0.5034779936072653
 ---
-# Language Models Fine-tuning on Question Generation: `lmqg/mbart-large-cc25-frquad`
 This model is fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) for question generation task on the
-[lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) (dataset_name: default).
 ### Overview
 - **Language model:** [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25)
 - **Language:** fr
@@ -71,6 +89,7 @@ pipe = pipeline("text2text-generation", model_path)
 # Question Generation
 question = pipe('Créateur » (Maker), lui aussi au singulier, « <hl> le Suprême Berger <hl> » (The Great Shepherd) ; de l'autre, des réminiscences de la théologie de l'Antiquité : le tonnerre, voix de Jupiter, « Et souvent ta voix gronde en un tonnerre terrifiant », etc.')
 ```
 ## Evaluation Metrics
@@ -107,4 +126,16 @@ The following hyperparameters were used during fine-tuning:
 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mbart-large-cc25-frquad/raw/main/trainer_config.json).
 ## Citation
-TBA

       value: 0.5034779936072653
 ---
+# Model Card of `lmqg/mbart-large-cc25-frquad`
 This model is fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) for question generation task on the
+[lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
+Please cite our paper if you use the model ([TBA](TBA)).
+```
+@inproceedings{ushio-etal-2022-generative,
+    title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration: {A} {U}nified {B}enchmark and {E}valuation",
+    author = "Ushio, Asahi  and
+        Alva-Manchego, Fernando and
+        Camacho-Collados, Jose",
+    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
+    month = dec,
+    year = "2022",
+    address = "Abu Dhabi, U.A.E.",
+    publisher = "Association for Computational Linguistics",
+}
+```
 ### Overview
 - **Language model:** [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25)
 - **Language:** fr
 # Question Generation
 question = pipe('Créateur » (Maker), lui aussi au singulier, « <hl> le Suprême Berger <hl> » (The Great Shepherd) ; de l'autre, des réminiscences de la théologie de l'Antiquité : le tonnerre, voix de Jupiter, « Et souvent ta voix gronde en un tonnerre terrifiant », etc.')
 ```
 ## Evaluation Metrics
 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mbart-large-cc25-frquad/raw/main/trainer_config.json).
 ## Citation
+@inproceedings{ushio-etal-2022-generative,
+    title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration: {A} {U}nified {B}enchmark and {E}valuation",
+    author = "Ushio, Asahi  and
+        Alva-Manchego, Fernando and
+        Camacho-Collados, Jose",
+    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
+    month = dec,
+    year = "2022",
+    address = "Abu Dhabi, U.A.E.",
+    publisher = "Association for Computational Linguistics",
+}