Instructions to use CLMBR/superlative-quantifier-transformer-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CLMBR/superlative-quantifier-transformer-2 with Transformers:

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

pipe = pipeline("text-generation", model="CLMBR/superlative-quantifier-transformer-2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CLMBR/superlative-quantifier-transformer-2")
model = AutoModelForCausalLM.from_pretrained("CLMBR/superlative-quantifier-transformer-2")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use CLMBR/superlative-quantifier-transformer-2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CLMBR/superlative-quantifier-transformer-2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CLMBR/superlative-quantifier-transformer-2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/CLMBR/superlative-quantifier-transformer-2

SGLang

How to use CLMBR/superlative-quantifier-transformer-2 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 "CLMBR/superlative-quantifier-transformer-2" \
    --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": "CLMBR/superlative-quantifier-transformer-2",
		"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 "CLMBR/superlative-quantifier-transformer-2" \
        --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": "CLMBR/superlative-quantifier-transformer-2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use CLMBR/superlative-quantifier-transformer-2 with Docker Model Runner:
```
docker model run hf.co/CLMBR/superlative-quantifier-transformer-2
```

abhinavp commited on Jan 18, 2024

Commit

e5d987c

verified ·

1 Parent(s): 486db1d

Training in progress, epoch 1

Browse files

Files changed (1) hide show

pytorch_model.bin +1 -1

pytorch_model.bin CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:a161f787615a1be7c5e3add2f6ef094e1bc888004071399bc3049f591e247199
 size 268727709

 version https://git-lfs.github.com/spec/v1
+oid sha256:1e9b6e97cd761dd27d372c7706be993a6802f780d19ad011ab0ab20e1ff9de9b
 size 268727709