Instructions to use llmware/slim-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use llmware/slim-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="llmware/slim-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("llmware/slim-ner") model = AutoModelForCausalLM.from_pretrained("llmware/slim-ner") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use llmware/slim-ner with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "llmware/slim-ner" # 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-ner", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/llmware/slim-ner
- SGLang
How to use llmware/slim-ner 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-ner" \ --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-ner", "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-ner" \ --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-ner", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use llmware/slim-ner with Docker Model Runner:
docker model run hf.co/llmware/slim-ner
Update README.md
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README.md
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<!-- Provide a quick summary of what the model is/does. -->
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**slim-ner** is part of the SLIM ("**S**tructured **L**anguage **I**nstruction **M**odel") model series, consisting of small, specialized decoder-based models, fine-tuned for function-calling.
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slim-ner has been fine-tuned for **named entity extraction** function calls, generating output consisting of a python dictionary corresponding to specified keys, e.g.:
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SLIM models are designed to generate structured outputs that can be used programmatically as part of a multi-step, multi-model LLM-based automation workflow.
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Each slim model has a 'quantized tool' version, e.g., [**'slim-ner-tool'**](https://huggingface.co/llmware/slim-ner-tool).
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<!-- Provide a quick summary of what the model is/does. -->
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**slim-ner** is part of the SLIM ("**S**tructured **L**anguage **I**nstruction **M**odel") model series, consisting of 1b parameter small, specialized decoder-based models, fine-tuned for function-calling.
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slim-ner has been fine-tuned for **named entity extraction** function calls, generating output consisting of a python dictionary corresponding to specified keys, e.g.:
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SLIM models are designed to generate structured outputs that can be used programmatically as part of a multi-step, multi-model LLM-based automation workflow.
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SLIM models can be used 'out of the box' for rapid prototyping in most general purpose use cases, and are designed to serve as a solid base that can be easily fine-tuned and adapted for specialized production use cases.
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Each slim model has a 'quantized tool' version, e.g., [**'slim-ner-tool'**](https://huggingface.co/llmware/slim-ner-tool).
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