Instructions to use Minibase/NER-Standard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Minibase/NER-Standard with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Minibase/NER-Standard",
	filename="model.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Minibase/NER-Standard with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Minibase/NER-Standard
# Run inference directly in the terminal:
llama-cli -hf Minibase/NER-Standard

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Minibase/NER-Standard
# Run inference directly in the terminal:
llama-cli -hf Minibase/NER-Standard

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Minibase/NER-Standard
# Run inference directly in the terminal:
./llama-cli -hf Minibase/NER-Standard

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Minibase/NER-Standard
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Minibase/NER-Standard

Use Docker

docker model run hf.co/Minibase/NER-Standard

LM Studio
Jan

vLLM

How to use Minibase/NER-Standard with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Minibase/NER-Standard

Ollama
How to use Minibase/NER-Standard with Ollama:
```
ollama run hf.co/Minibase/NER-Standard
```

Unsloth Studio new

How to use Minibase/NER-Standard with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Minibase/NER-Standard to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Minibase/NER-Standard to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Minibase/NER-Standard to start chatting

Docker Model Runner
How to use Minibase/NER-Standard with Docker Model Runner:
```
docker model run hf.co/Minibase/NER-Standard
```

Lemonade

How to use Minibase/NER-Standard with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Minibase/NER-Standard

Run and chat with the model

lemonade run user.NER-Standard-{{QUANT_TAG}}

List all available models

lemonade list

Minibase commited on Oct 7, 2025

Commit

88f0131

verified ·

1 Parent(s): 35a6bef

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benchmarks.txt +48 -0

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+# NER Benchmark Results
+**Model:** Minibase-NER-Standard
+**Dataset:** ner_benchmark_dataset.jsonl
+**Sample Size:** 100
+**Date:** 2025-10-07T13:41:36.866891
+## Overall Performance
+| Metric | Score | Description |
+|--------|-------|-------------|
+| F1 Score | 0.951 | Overall NER performance (harmonic mean of precision and recall) |
+| Precision | 0.915 | Accuracy of entity predictions |
+| Recall | 1.000 | Ability to find all entities |
+| Average Latency | 323.3ms | Response time performance |
+## Entity Type Performance
+| Entity Type | Accuracy | Correct/Total |
+|-------------|----------|---------------|
+| PERSON | 1.000 | 100/100 |
+| ORG | 1.000 | 100/100 |
+| LOC | 0.660 | 66/100 |
+| MISC | 1.000 | 34/34 |
+## Key Improvements
+- **BIO Tagging**: Model outputs entities in BIO (Beginning-Inside-Outside) format
+- **Multiple Entity Types**: Supports PERSON, ORG, LOC, and MISC entities
+- **Entity-Level Evaluation**: Metrics calculated at entity level rather than token level
+- **Comprehensive Coverage**: Evaluates across different text domains
+## Example Results
+### Example 1
+**Input:** John Smith works at Google in New York and uses Python programming language....
+**Predicted:** { "PER": ["John Smith"], "ORG": ["Google"], "LOC": ["New York"], "MISC": ["Python"] }...
+**F1 Score:** 0.857
+### Example 2
+**Input:** Microsoft Corporation announced that Satya Nadella will visit London next week....
+**Predicted:** { "PER": ["Satya Nadella"], "ORG": ["Microsoft Corporation"], "LOC": ["London"], "MISC": [] }...
+**F1 Score:** 1.000
+### Example 3
+**Input:** The University of Cambridge is located in the United Kingdom and was founded by King Henry III....
+**Predicted:** { "PER": ["King Henry III"], "ORG": ["University of Cambridge"], "LOC": ["United Kingdom"], "MISC": [] }...
+**F1 Score:** 1.000