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

Libraries

How to use llmware/slim-q-gen-tiny-ov with Transformers:

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

pipe = pipeline("text-generation", model="llmware/slim-q-gen-tiny-ov")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("llmware/slim-q-gen-tiny-ov")
model = AutoModelForCausalLM.from_pretrained("llmware/slim-q-gen-tiny-ov")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use llmware/slim-q-gen-tiny-ov with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "llmware/slim-q-gen-tiny-ov"
# 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-q-gen-tiny-ov",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/llmware/slim-q-gen-tiny-ov

SGLang

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

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

doberst commited on Oct 11, 2024

Commit

c17a0e3

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1 Parent(s): 308eafb

Update README.md

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@@ -4,9 +4,9 @@ inference: false
 tags: [green, p1, llmware-fx, ov, emerald]
 ---
-# slim-extract-tiny-ov
-**slim-extract-tiny-ov** is a specialized function calling model that implements a generative 'question' (e.g., 'q-gen') function, which takes a context passage as an input, and then generates as an output a python dictionary consisting of one key:
  `{'question': ['What was the amount of revenue in the quarter?']} `

 tags: [green, p1, llmware-fx, ov, emerald]
 ---
+# slim-q-gen-tiny-ov
+**slim-q-gen-tiny-ov** is a specialized function calling model that implements a generative 'question' (e.g., 'q-gen') function, which takes a context passage as an input, and then generates as an output a python dictionary consisting of one key:
  `{'question': ['What was the amount of revenue in the quarter?']} `