Instructions to use shahules786/Safetybot-mt5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shahules786/Safetybot-mt5-base with Transformers:

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

pipe = pipeline("text-generation", model="shahules786/Safetybot-mt5-base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("shahules786/Safetybot-mt5-base")
model = AutoModelForSeq2SeqLM.from_pretrained("shahules786/Safetybot-mt5-base")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use shahules786/Safetybot-mt5-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shahules786/Safetybot-mt5-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shahules786/Safetybot-mt5-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/shahules786/Safetybot-mt5-base

SGLang

How to use shahules786/Safetybot-mt5-base 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 "shahules786/Safetybot-mt5-base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shahules786/Safetybot-mt5-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "shahules786/Safetybot-mt5-base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shahules786/Safetybot-mt5-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use shahules786/Safetybot-mt5-base with Docker Model Runner:
```
docker model run hf.co/shahules786/Safetybot-mt5-base
```

shahules786 commited on Mar 8, 2023

Commit

3a6b70b

1 Parent(s): b4f2ae2

Upload config.json with huggingface_hub

Browse files

Files changed (1) hide show

config.json +7 -5

config.json CHANGED Viewed

@@ -1,21 +1,21 @@
 {
   "_name_or_path": "t5-base",
   "architectures": [
-    "T5ForConditionalGeneration"
   ],
   "d_ff": 3072,
   "d_kv": 64,
   "d_model": 768,
   "decoder_start_token_id": 0,
-  "dense_act_fn": "relu",
   "dropout_rate": 0.1,
   "eos_token_id": 1,
-  "feed_forward_proj": "relu",
   "initializer_factor": 1.0,
   "is_encoder_decoder": true,
-  "is_gated_act": false,
   "layer_norm_epsilon": 1e-06,
-  "model_type": "t5",
   "n_positions": 512,
   "num_decoder_layers": 12,
   "num_heads": 12,
@@ -53,6 +53,8 @@
       "prefix": "translate English to Romanian: "
     }
   },
   "torch_dtype": "float32",
   "transformers_version": "4.26.1",
   "use_cache": true,

 {
   "_name_or_path": "t5-base",
   "architectures": [
+    "MT5ForConditionalGeneration"
   ],
   "d_ff": 3072,
   "d_kv": 64,
   "d_model": 768,
   "decoder_start_token_id": 0,
+  "dense_act_fn": "gelu_new",
   "dropout_rate": 0.1,
   "eos_token_id": 1,
+  "feed_forward_proj": "gated-gelu",
   "initializer_factor": 1.0,
   "is_encoder_decoder": true,
+  "is_gated_act": true,
   "layer_norm_epsilon": 1e-06,
+  "model_type": "mt5",
   "n_positions": 512,
   "num_decoder_layers": 12,
   "num_heads": 12,
       "prefix": "translate English to Romanian: "
     }
   },
+  "tie_word_embeddings": false,
+  "tokenizer_class": "T5Tokenizer",
   "torch_dtype": "float32",
   "transformers_version": "4.26.1",
   "use_cache": true,