Instructions to use brandolorian/answerQuestioning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use brandolorian/answerQuestioning with Transformers:

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

pipe = pipeline("text-generation", model="brandolorian/answerQuestioning")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("brandolorian/answerQuestioning")
model = AutoModelForCausalLM.from_pretrained("brandolorian/answerQuestioning")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use brandolorian/answerQuestioning with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/brandolorian/answerQuestioning

SGLang

How to use brandolorian/answerQuestioning 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 "brandolorian/answerQuestioning" \
    --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": "brandolorian/answerQuestioning",
		"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 "brandolorian/answerQuestioning" \
        --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": "brandolorian/answerQuestioning",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use brandolorian/answerQuestioning with Docker Model Runner:
```
docker model run hf.co/brandolorian/answerQuestioning
```

brandolorian commited on Feb 19, 2024

Commit

6f8479f

verified ·

1 Parent(s): 3585797

Training in progress, step 32500

Browse files

Files changed (2) hide show

model.safetensors +1 -1
runs/Feb19_02-21-19_61a4bf2b3b7a/events.out.tfevents.1708309281.61a4bf2b3b7a.781.0 +2 -2

model.safetensors CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:e91df621bd071acbf61b9291de78a82c70379306f7decce9acbd6f0833504d1c
 size 274062040

 version https://git-lfs.github.com/spec/v1
+oid sha256:aa073e3424b855444743fc6c7d69faf6772f9351ac8dd94927f4dd27fb063192
 size 274062040

runs/Feb19_02-21-19_61a4bf2b3b7a/events.out.tfevents.1708309281.61a4bf2b3b7a.781.0 CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:91623ea7e504b14d8d49fc1f57328620a30bf7bb59fd422f12e042b7102cf301
-size 17805

 version https://git-lfs.github.com/spec/v1
+oid sha256:d4ce7bdfff06ba6c1c27e748e2fee1de49bf5581c3a42778a351a841f94c6218
+size 17965