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

Libraries

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

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("llmware/slim-qa-gen-tiny-ov")
model = AutoModelForCausalLM.from_pretrained("llmware/slim-qa-gen-tiny-ov", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

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

SGLang

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

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

doberst commited on Oct 11, 2024

Commit

a746a5a

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1 Parent(s): 9321677

Update README.md

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@@ -1,14 +1,14 @@
 ---
 license: apache-2.0
 inference: false
-tags: [green, p1, llmware-fx, ov, emerald]
 ---
-# slim-extract-tiny-ov
-**slim-extract-tiny-ov** is a specialized function calling model with a single mission to look for values in a text, based on an "extract" key that is passed as a parameter.  No other instructions are required except to pass the context passage, and the target key, and the model will generate a python dictionary consisting of the extract key and a list of the values found in the text, including an 'empty list' if the text does not provide an answer for the value of the selected key.
-This is an OpenVino int4 quantized version of slim-extract-tiny, providing a very fast, very small inference implementation, optimized for AI PCs using Intel GPU, CPU and NPU.
 ### Model Description
@@ -16,24 +16,13 @@ This is an OpenVino int4 quantized version of slim-extract-tiny, providing a ver
 - **Developed by:** llmware
 - **Model type:** tinyllama
 - **Parameters:** 1.1 billion
-- **Model Parent:** llmware/slim-extract-tiny
 - **Language(s) (NLP):** English
 - **License:** Apache 2.0
-- **Uses:** Extraction of values from complex business documents
 - **RAG Benchmark Accuracy Score:** NA
 - **Quantization:** int4
-### Example Usage
-    from llmware.models import ModelCatalog
-    text_passage = "The company announced that for the current quarter the total revenue increased by 9% to $125 million."
-    model = ModelCatalog().load_model("slim-extract-tiny-ov")
-    llm_response = model.function_call(text_passage, function="extract", params=["revenue"])
-    Output: `llm_response = {"revenue": [$125 million"]}`
 ## Model Card Contact
 [llmware on github](https://www.github.com/llmware-ai/llmware)

 ---
 license: apache-2.0
 inference: false
+tags: [green, p1, llmware-fx, ov]
 ---
+# slim-qa-gen-tiny-ov
+**slim-qa-gen-tiny-ov** is a specialized function calling model that generates a question and answer pair from a context passage.
+This is an OpenVino int4 quantized version of slim-qa-gen-tiny, providing a very fast, very small inference implementation, optimized for AI PCs using Intel GPU, CPU and NPU.
 ### Model Description
 - **Developed by:** llmware
 - **Model type:** tinyllama
 - **Parameters:** 1.1 billion
+- **Model Parent:** llmware/slim-qa-gen-tiny
 - **Language(s) (NLP):** English
 - **License:** Apache 2.0
+- **Uses:** Automated generation of question-answer pairs from complex business documents
 - **RAG Benchmark Accuracy Score:** NA
 - **Quantization:** int4
 ## Model Card Contact
 [llmware on github](https://www.github.com/llmware-ai/llmware)