Instructions to use llmware/slim-extract-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use llmware/slim-extract-tiny with Transformers:

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

pipe = pipeline("text-generation", model="llmware/slim-extract-tiny")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("llmware/slim-extract-tiny")
model = AutoModelForCausalLM.from_pretrained("llmware/slim-extract-tiny")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use llmware/slim-extract-tiny with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/llmware/slim-extract-tiny

SGLang

How to use llmware/slim-extract-tiny 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 "llmware/slim-extract-tiny" \
    --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": "llmware/slim-extract-tiny",
		"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 "llmware/slim-extract-tiny" \
        --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": "llmware/slim-extract-tiny",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use llmware/slim-extract-tiny with Docker Model Runner:
```
docker model run hf.co/llmware/slim-extract-tiny
```

doberst commited on Jun 21, 2024

Commit

a2dcdb8

verified ·

1 Parent(s): 46cb2e1

Update config.json

Browse files

Files changed (1) hide show

config.json +0 -6

config.json CHANGED Viewed

@@ -1,10 +1,4 @@
 {
-  "aib_version": "model_archive_061224_tl_extract_0",
-  "training_dataset": [
-    "extract_tl_061224_eos_9442.jsonl"
-  ],
-  "training_timestamp": "Wed Jun 12 06:28:19 2024",
-  "training_comments": "tl-extract-061224-0",
   "vocab_size": 32000,
   "max_position_embeddings": 2048,
   "hidden_size": 2048,

 {
   "vocab_size": 32000,
   "max_position_embeddings": 2048,
   "hidden_size": 2048,