Instructions to use cstr/bert-base-NER-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cstr/bert-base-NER-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="cstr/bert-base-NER-GGUF",
	filename="bert-base-ner-f32.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use cstr/bert-base-NER-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf cstr/bert-base-NER-GGUF:F32
# Run inference directly in the terminal:
llama cli -hf cstr/bert-base-NER-GGUF:F32

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf cstr/bert-base-NER-GGUF:F32
# Run inference directly in the terminal:
llama cli -hf cstr/bert-base-NER-GGUF:F32

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 cstr/bert-base-NER-GGUF:F32
# Run inference directly in the terminal:
./llama-cli -hf cstr/bert-base-NER-GGUF:F32

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 cstr/bert-base-NER-GGUF:F32
# Run inference directly in the terminal:
./build/bin/llama-cli -hf cstr/bert-base-NER-GGUF:F32

Use Docker

docker model run hf.co/cstr/bert-base-NER-GGUF:F32

LM Studio
Jan
Ollama
How to use cstr/bert-base-NER-GGUF with Ollama:
```
ollama run hf.co/cstr/bert-base-NER-GGUF:F32
```

Unsloth Studio

How to use cstr/bert-base-NER-GGUF 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 cstr/bert-base-NER-GGUF 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 cstr/bert-base-NER-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

Atomic Chat new
Docker Model Runner
How to use cstr/bert-base-NER-GGUF with Docker Model Runner:
```
docker model run hf.co/cstr/bert-base-NER-GGUF:F32
```

Lemonade

How to use cstr/bert-base-NER-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull cstr/bert-base-NER-GGUF:F32

Run and chat with the model

lemonade run user.bert-base-NER-GGUF-F32

List all available models

lemonade list

BERT Base NER — GGUF

GGUF conversion of dslim/bert-base-NER for use with CrispEmbed.

Fixed-label Named Entity Recognition on English text. BERT-base-cased (110M params) fine-tuned on CoNLL-03 with 9 IOB labels.

Labels

ID	Label	Description
0	O	Outside any entity
1	B-MISC	Beginning of miscellaneous entity
2	I-MISC	Inside miscellaneous entity
3	B-PER	Beginning of person name
4	I-PER	Inside person name
5	B-ORG	Beginning of organization
6	I-ORG	Inside organization
7	B-LOC	Beginning of location
8	I-LOC	Inside location

Available Formats

File	Format	Size
`bert-base-ner-f32.gguf`	Float32	412 MB
`bert-base-ner-q8_0.gguf`	Q8_0	111 MB
`bert-base-ner-q4_k.gguf`	Q4_K	70 MB

Usage

crispembed -m bert-base-ner-q8_0.gguf --ner "Barack Obama was born in Hawaii"

from crispembed import CrispNER
ner = CrispNER("bert-base-ner-q8_0.gguf")
entities = ner.extract("Barack Obama was born in Hawaii")
# [{"text": "Barack Obama", "label": "PER", "start": 0, "end": 12, "score": 0.999},
#  {"text": "Hawaii", "label": "LOC", "start": 25, "end": 31, "score": 1.000}]

Auto-detected as BERT NER (vs GLiNER zero-shot) from ner.classifier.weight in GGUF.

Parity

Encoder output: cos_min=0.999971 vs HuggingFace transformers (F32).

Downloads last month: 137

GGUF

Model size

0.1B params

Architecture

bert

Hardware compatibility

8-bit

32-bit

Model tree for cstr/bert-base-NER-GGUF

Base model

dslim/bert-base-NER

Quantized

(5)

this model