Instructions to use razerblade072611/BertForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use razerblade072611/BertForSequenceClassification with Transformers:

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

pipe = pipeline("text-generation", model="razerblade072611/BertForSequenceClassification")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("razerblade072611/BertForSequenceClassification")
model = AutoModelForCausalLM.from_pretrained("razerblade072611/BertForSequenceClassification")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use razerblade072611/BertForSequenceClassification with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "razerblade072611/BertForSequenceClassification"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "razerblade072611/BertForSequenceClassification",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/razerblade072611/BertForSequenceClassification

SGLang

How to use razerblade072611/BertForSequenceClassification 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 "razerblade072611/BertForSequenceClassification" \
    --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": "razerblade072611/BertForSequenceClassification",
		"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 "razerblade072611/BertForSequenceClassification" \
        --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": "razerblade072611/BertForSequenceClassification",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use razerblade072611/BertForSequenceClassification with Docker Model Runner:
```
docker model run hf.co/razerblade072611/BertForSequenceClassification
```

razerblade072611 commited on May 20, 2023

Commit

c1f8e0b

1 Parent(s): f9664c4

Delete config.json

Browse files

Files changed (1) hide show

config.json +0 -27

config.json DELETED Viewed

@@ -1,27 +0,0 @@
-{
-  "_name_or_path": "bert-base-uncased",
-  "architectures": [
-    "BertForSequenceClassification"
-  ],
-  "attention_probs_dropout_prob": 0.1,
-  "classifier_dropout": 0.3,
-  "gradient_checkpointing": false,
-  "hidden_act": "gelu",
-  "hidden_dropout_prob": 0.1,
-  "hidden_size": 768,
-  "initializer_range": 0.02,
-  "intermediate_size": 3072,
-  "layer_norm_eps": 1e-12,
-  "max_position_embeddings": 512,
-  "model_type": "bert",
-  "num_attention_heads": 12,
-  "num_hidden_layers": 12,
-  "pad_token_id": 0,
-  "position_embedding_type": "absolute",
-  "problem_type": "single_label_classification",
-  "torch_dtype": "float32",
-  "transformers_version": "4.28.1",
-  "type_vocab_size": 2,
-  "use_cache": true,
-  "vocab_size": 30522
-}