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

Libraries

How to use research-backup/mbart-large-cc25-esquad-qag with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

docker model run hf.co/research-backup/mbart-large-cc25-esquad-qag

SGLang

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

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

asahi417 commited on Jan 18, 2023

Commit

16ec783

1 Parent(s): ac4e5b6

model update

Browse files

Files changed (1) hide show

README.md +0 -23

README.md CHANGED Viewed

@@ -27,21 +27,6 @@ model-index:
       type: default
       args: default
     metrics:
-    - name: BLEU4 (Question & Answer Generation)
-      type: bleu4_question_answer_generation
-      value: 2.92
-    - name: ROUGE-L (Question & Answer Generation)
-      type: rouge_l_question_answer_generation
-      value: 16.2
-    - name: METEOR (Question & Answer Generation)
-      type: meteor_question_answer_generation
-      value: 23.46
-    - name: BERTScore (Question & Answer Generation)
-      type: bertscore_question_answer_generation
-      value: 69.37
-    - name: MoverScore (Question & Answer Generation)
-      type: moverscore_question_answer_generation
-      value: 51.66
     - name: QAAlignedF1Score-BERTScore (Question & Answer Generation)
       type: qa_aligned_f1_score_bertscore_question_answer_generation
       value: 78.8
@@ -103,20 +88,12 @@ output = pipe("del Ministerio de Desarrollo Urbano , Gobierno de la India.")
 |                                 |   Score | Type    | Dataset                                                            |
 |:--------------------------------|--------:|:--------|:-------------------------------------------------------------------|
-| BERTScore                       |   69.37 | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) |
-| Bleu_1                          |   11.18 | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) |
-| Bleu_2                          |    6.31 | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) |
-| Bleu_3                          |    4.12 | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) |
-| Bleu_4                          |    2.92 | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) |
-| METEOR                          |   23.46 | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) |
-| MoverScore                      |   51.66 | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) |
 | QAAlignedF1Score (BERTScore)    |   78.8  | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) |
 | QAAlignedF1Score (MoverScore)   |   54    | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) |
 | QAAlignedPrecision (BERTScore)  |   76.59 | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) |
 | QAAlignedPrecision (MoverScore) |   52.57 | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) |
 | QAAlignedRecall (BERTScore)     |   81.21 | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) |
 | QAAlignedRecall (MoverScore)    |   55.63 | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) |
-| ROUGE_L                         |   16.2  | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) |

       type: default
       args: default
     metrics:
     - name: QAAlignedF1Score-BERTScore (Question & Answer Generation)
       type: qa_aligned_f1_score_bertscore_question_answer_generation
       value: 78.8
 |                                 |   Score | Type    | Dataset                                                            |
 |:--------------------------------|--------:|:--------|:-------------------------------------------------------------------|
 | QAAlignedF1Score (BERTScore)    |   78.8  | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) |
 | QAAlignedF1Score (MoverScore)   |   54    | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) |
 | QAAlignedPrecision (BERTScore)  |   76.59 | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) |
 | QAAlignedPrecision (MoverScore) |   52.57 | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) |
 | QAAlignedRecall (BERTScore)     |   81.21 | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) |
 | QAAlignedRecall (MoverScore)    |   55.63 | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) |