Instructions to use RichardErkhov/szymonrucinski_-_Curie-7B-v1-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RichardErkhov/szymonrucinski_-_Curie-7B-v1-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="RichardErkhov/szymonrucinski_-_Curie-7B-v1-gguf",
	filename="Curie-7B-v1.IQ3_M.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 RichardErkhov/szymonrucinski_-_Curie-7B-v1-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf RichardErkhov/szymonrucinski_-_Curie-7B-v1-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf RichardErkhov/szymonrucinski_-_Curie-7B-v1-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf RichardErkhov/szymonrucinski_-_Curie-7B-v1-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf RichardErkhov/szymonrucinski_-_Curie-7B-v1-gguf:Q4_K_M

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 RichardErkhov/szymonrucinski_-_Curie-7B-v1-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf RichardErkhov/szymonrucinski_-_Curie-7B-v1-gguf:Q4_K_M

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 RichardErkhov/szymonrucinski_-_Curie-7B-v1-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RichardErkhov/szymonrucinski_-_Curie-7B-v1-gguf:Q4_K_M

Use Docker

docker model run hf.co/RichardErkhov/szymonrucinski_-_Curie-7B-v1-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use RichardErkhov/szymonrucinski_-_Curie-7B-v1-gguf with Ollama:
```
ollama run hf.co/RichardErkhov/szymonrucinski_-_Curie-7B-v1-gguf:Q4_K_M
```

Unsloth Studio

How to use RichardErkhov/szymonrucinski_-_Curie-7B-v1-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 RichardErkhov/szymonrucinski_-_Curie-7B-v1-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 RichardErkhov/szymonrucinski_-_Curie-7B-v1-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for RichardErkhov/szymonrucinski_-_Curie-7B-v1-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use RichardErkhov/szymonrucinski_-_Curie-7B-v1-gguf with Docker Model Runner:
```
docker model run hf.co/RichardErkhov/szymonrucinski_-_Curie-7B-v1-gguf:Q4_K_M
```

Lemonade

How to use RichardErkhov/szymonrucinski_-_Curie-7B-v1-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RichardErkhov/szymonrucinski_-_Curie-7B-v1-gguf:Q4_K_M

Run and chat with the model

lemonade run user.szymonrucinski_-_Curie-7B-v1-gguf-Q4_K_M

List all available models

lemonade list

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Quantization made by Richard Erkhov.

Github

Discord

Request more models

Curie-7B-v1 - GGUF

Model creator: https://huggingface.co/szymonrucinski/
Original model: https://huggingface.co/szymonrucinski/Curie-7B-v1/

Name	Quant method	Size
Curie-7B-v1.Q2_K.gguf	Q2_K	2.53GB
Curie-7B-v1.IQ3_XS.gguf	IQ3_XS	2.81GB
Curie-7B-v1.IQ3_S.gguf	IQ3_S	2.96GB
Curie-7B-v1.Q3_K_S.gguf	Q3_K_S	2.95GB
Curie-7B-v1.IQ3_M.gguf	IQ3_M	3.06GB
Curie-7B-v1.Q3_K.gguf	Q3_K	3.28GB
Curie-7B-v1.Q3_K_M.gguf	Q3_K_M	3.28GB
Curie-7B-v1.Q3_K_L.gguf	Q3_K_L	3.56GB
Curie-7B-v1.IQ4_XS.gguf	IQ4_XS	3.67GB
Curie-7B-v1.Q4_0.gguf	Q4_0	3.83GB
Curie-7B-v1.IQ4_NL.gguf	IQ4_NL	3.87GB
Curie-7B-v1.Q4_K_S.gguf	Q4_K_S	3.86GB
Curie-7B-v1.Q4_K.gguf	Q4_K	4.07GB
Curie-7B-v1.Q4_K_M.gguf	Q4_K_M	4.07GB
Curie-7B-v1.Q4_1.gguf	Q4_1	4.24GB
Curie-7B-v1.Q5_0.gguf	Q5_0	4.65GB
Curie-7B-v1.Q5_K_S.gguf	Q5_K_S	4.65GB
Curie-7B-v1.Q5_K.gguf	Q5_K	4.78GB
Curie-7B-v1.Q5_K_M.gguf	Q5_K_M	4.78GB
Curie-7B-v1.Q5_1.gguf	Q5_1	5.07GB
Curie-7B-v1.Q6_K.gguf	Q6_K	5.53GB

Original model description:

license: apache-2.0 language: - pl library_name: transformers tags: - polish - nlp

Curie-7B-v1

Introduction

This research demonstrates the potential of fine-tuning English Large Language Models (LLMs) for Polish text generation. By employing Language Adaptive Pre-training (LAPT) on a high-quality dataset of 3.11 GB (276 million Polish tokens) and subsequent fine-tuning on the KLEJ challenges, the Curie-7B-v1 model achieves remarkable performance. It not only generates Polish text with the lowest perplexity of 3.02 among decoder-based models but also rivals the best Polish encoder-decoder models closely, with a minimal performance gap on 8 out of 9 tasks. This was accomplished using about 2-3% of the dataset size typically required, showcasing the method's efficiency. The model is now open-source, contributing to the community's collaborative progress.

Language Adaptive Pre-training Dataset

The LAPT phase utilized the SpeakLeash dataset, a comprehensive collection of Polish texts, focusing on the highest quality extract of approximately 2 GB from the original 1TB.

Hardware and Software Stack

Experiments were conducted on a server featuring an NVIDIA RTX A6000 ADA GPU with 48GB of VRAM, AMD Epyc 7742 processor, and running Ubuntu with Pytorch 2.0 and CUDA 12.2.

The Adaptive Pre-training

The model was trained using AdamW optimizer, with specific hyperparameters aimed at optimizing performance. Training completed in one epoch, taking a total of 106 hours, demonstrating the onset of overfitting beyond this point.

Hyperparameters

lora_rank: 32
lora_dropout: 0.05
lora_alpha: 16
warmup_steps: 0.1
learning_rate: 2.5 x 10^-5
neftune_noise_alpha: 2
batch_size: 128
max_seq_len: 128

Fine-tuning for KLEJ Downstream Tasks

Curie-7B-v1 was exceptionally close to the best baseline models on 8 of 9 KLEJ tasks by using significantly less data, showcasing its efficiency and capability in handling a variety of NLP tasks in Polish.

Performance Highlights

NKJP-NER: 93.4
CDSC-E: 92.2
CDSC-R: 94.9
CBD: 49.0 (Demonstrating room for improvement)
PolEmo2.0-IN: 92.7
PolEmo2.0-OUT: 80.0
DYK: 76.2
PSC: 98.6
AR: 86.8

Conclusions

The Curie-7B-v1 model, through LAPT, matches foundational models on eight downstream tasks with significantly less data. Its versatility in generating Polish text and the ability to be transformed into classifiers, regressors, and AI assistants highlights the method's effectiveness. This open-source Polish LLM provides a foundation for developing efficient business solutions.

Research Paper

Work and details regarding this model are described in the reserach paper Efficient Language Adaptive Pre-training: Extending State-of-the-Art Large Language Models for Polish by Szymon Ruciński.

Downloads last month: 439

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Paper for RichardErkhov/szymonrucinski_-_Curie-7B-v1-gguf

Efficient Language Adaptive Pre-training: Extending State-of-the-Art Large Language Models for Polish

Paper • 2402.09759 • Published Feb 15, 2024 • 1