Instructions to use Dans-DiscountModels/Dans-TextSplitter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Dans-DiscountModels/Dans-TextSplitter with Transformers:

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

pipe = pipeline("text-generation", model="Dans-DiscountModels/Dans-TextSplitter")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Dans-DiscountModels/Dans-TextSplitter")
model = AutoModelForCausalLM.from_pretrained("Dans-DiscountModels/Dans-TextSplitter")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Dans-DiscountModels/Dans-TextSplitter with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Dans-DiscountModels/Dans-TextSplitter

SGLang

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

Docker Model Runner
How to use Dans-DiscountModels/Dans-TextSplitter with Docker Model Runner:
```
docker model run hf.co/Dans-DiscountModels/Dans-TextSplitter
```

PocketDoc commited on Feb 18, 2024

Commit

6f95051

verified ·

1 Parent(s): f7f2036

Delete tokenizer_config.json

Browse files

Files changed (1) hide show

tokenizer_config.json +0 -15

tokenizer_config.json DELETED Viewed

@@ -1,15 +0,0 @@
-{
-  "added_tokens_decoder": {},
-  "auto_map": {
-    "AutoTokenizer": [
-      "stabilityai/stablelm-2-1_6b--tokenization_arcade100k.Arcade100kTokenizer",
-      null
-    ]
-  },
-  "clean_up_tokenization_spaces": true,
-  "eos_token": "<|endoftext|>",
-  "errors": "replace",
-  "model_max_length": 1000000000000000019884624838656,
-  "pad_token": "<|endoftext|>",
-  "tokenizer_class": "Arcade100kTokenizer"
-}