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 Settings

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
```

slim-extract-tiny / README.md

doberst

Update README.md

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	---
	license: apache-2.0
	inference: false
	---

	# SLIM-EXTRACT-TINY

	<!-- Provide a quick summary of what the model is/does. -->

	slim-extract-tiny implements a specialized function-calling customizable 'extract' capability that takes as an input a context passage, a customized key, and outputs a python dictionary with key that corresponds to the customized key, with a value consisting of a list of items extracted from the text corresponding to that key, e.g.,

	`{'universities': ['Berkeley, Stanford, Yale, University of Florida, ...'] }`

	This model is fine-tuned on top of a tiny-llama 1b base.

	For fast inference use, we would recommend the 'quantized tool' version, e.g., ['slim-extract-tiny-tool'](https://huggingface.co/llmware/slim-extract-tiny-tool).


	## Prompt format:

	`function = "extract"`
	`params = "{custom key}"`
	`prompt = "<human> " + {text} + "\n" + `
	`"<{function}> " + {params} + "</{function}>" + "\n<bot>:"`


	<details>
	<summary>Transformers Script </summary>

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

	function = "extract"
	params = "company"

	text = "Tesla stock declined yesterday 8% in premarket trading after a poorly-received event in San Francisco yesterday, in which the company indicated a likely shortfall in revenue."

	prompt = "<human>: " + text + "\n" + f"<{function}> {params} </{function}>\n<bot>:"

	inputs = tokenizer(prompt, return_tensors="pt")
	start_of_input = len(inputs.input_ids[0])

	outputs = model.generate(
	inputs.input_ids.to('cpu'),
	eos_token_id=tokenizer.eos_token_id,
	pad_token_id=tokenizer.eos_token_id,
	do_sample=True,
	temperature=0.3,
	max_new_tokens=100
	)

	output_only = tokenizer.decode(outputs[0][start_of_input:], skip_special_tokens=True)

	print("output only: ", output_only)

	# here's the fun part
	try:
	output_only = ast.literal_eval(llm_string_output)
	print("success - converted to python dictionary automatically")
	except:
	print("fail - could not convert to python dictionary automatically - ", llm_string_output)

	</details>

	<details>




	<summary>Using as Function Call in LLMWare</summary>

	from llmware.models import ModelCatalog
	slim_model = ModelCatalog().load_model("llmware/slim-extract-tiny")
	response = slim_model.function_call(text,params=["company"], function="extract")

	print("llmware - llm_response: ", response)

	</details>


	## Model Card Contact

	Darren Oberst & llmware team

	[Join us on Discord](https://discord.gg/MhZn5Nc39h)