Instructions to use TableCheck/gemma-3-27b-it-ft-query-extraction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TableCheck/gemma-3-27b-it-ft-query-extraction with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-3-27b-it-unsloth-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "TableCheck/gemma-3-27b-it-ft-query-extraction")

Transformers

How to use TableCheck/gemma-3-27b-it-ft-query-extraction with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("TableCheck/gemma-3-27b-it-ft-query-extraction", dtype="auto")

llama-cpp-python

How to use TableCheck/gemma-3-27b-it-ft-query-extraction with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="TableCheck/gemma-3-27b-it-ft-query-extraction",
	filename="abc.BF16-00001-of-00002.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use TableCheck/gemma-3-27b-it-ft-query-extraction with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf TableCheck/gemma-3-27b-it-ft-query-extraction:BF16
# Run inference directly in the terminal:
llama-cli -hf TableCheck/gemma-3-27b-it-ft-query-extraction:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf TableCheck/gemma-3-27b-it-ft-query-extraction:BF16
# Run inference directly in the terminal:
llama-cli -hf TableCheck/gemma-3-27b-it-ft-query-extraction:BF16

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 TableCheck/gemma-3-27b-it-ft-query-extraction:BF16
# Run inference directly in the terminal:
./llama-cli -hf TableCheck/gemma-3-27b-it-ft-query-extraction:BF16

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 TableCheck/gemma-3-27b-it-ft-query-extraction:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf TableCheck/gemma-3-27b-it-ft-query-extraction:BF16

Use Docker

docker model run hf.co/TableCheck/gemma-3-27b-it-ft-query-extraction:BF16

LM Studio
Jan
Ollama
How to use TableCheck/gemma-3-27b-it-ft-query-extraction with Ollama:
```
ollama run hf.co/TableCheck/gemma-3-27b-it-ft-query-extraction:BF16
```

Unsloth Studio new

How to use TableCheck/gemma-3-27b-it-ft-query-extraction 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 TableCheck/gemma-3-27b-it-ft-query-extraction 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 TableCheck/gemma-3-27b-it-ft-query-extraction to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for TableCheck/gemma-3-27b-it-ft-query-extraction to start chatting

Docker Model Runner
How to use TableCheck/gemma-3-27b-it-ft-query-extraction with Docker Model Runner:
```
docker model run hf.co/TableCheck/gemma-3-27b-it-ft-query-extraction:BF16
```

Lemonade

How to use TableCheck/gemma-3-27b-it-ft-query-extraction with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull TableCheck/gemma-3-27b-it-ft-query-extraction:BF16

Run and chat with the model

lemonade run user.gemma-3-27b-it-ft-query-extraction-BF16

List all available models

lemonade list

Model Card for TableCheck's Fine-Tune of google/gemma-3-27b-it

This fine-tuned model is designed for use with function calls to translate content between languages and extract tags from content. It has been trained on public data.

Model Details

Model Description

Developed by: TableCheck AI
Funded by: TableCheck
Shared by: TableCheck
Model type: LoRa Adaptor
Language(s) (NLP): English / Japanese / Gemma 3 27B Supported Languages
License: Copyright TableCheck
Finetuned from model: google/gemma-3-27b-it

Model Sources [optional]

Trained from public data of venues.

How to Get Started with the Model

Use the code below to get started with the model. TBD

Training Details

Training Data

TBD

[More Information Needed]

Training Procedure

TBD

Training Hyperparameters

Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

[More Information Needed]

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: [More Information Needed]
Hours used: [More Information Needed]
Cloud Provider: [More Information Needed]
Compute Region: [More Information Needed]
Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

[More Information Needed]

Compute Infrastructure

[More Information Needed]

Hardware

[More Information Needed]

Software

[More Information Needed]

Citation [optional]

BibTeX:

[More Information Needed]

APA:

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Glossary [optional]

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More Information [optional]

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Model Card Authors [optional]

[More Information Needed]

Model Card Contact

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Framework versions

PEFT 0.15.0

Downloads last month: 7

GGUF

Model size

27B params

Architecture

gemma3

Hardware compatibility

16-bit

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Model tree for TableCheck/gemma-3-27b-it-ft-query-extraction

Base model

google/gemma-3-27b-pt

Finetuned

google/gemma-3-27b-it

Quantized

unsloth/gemma-3-27b-it-unsloth-bnb-4bit

Adapter

(65)

this model

Paper for TableCheck/gemma-3-27b-it-ft-query-extraction

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 46