Instructions to use allmalab/gpt2-aze with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use allmalab/gpt2-aze with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="allmalab/gpt2-aze")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("allmalab/gpt2-aze")
model = AutoModelForCausalLM.from_pretrained("allmalab/gpt2-aze")

llama-cpp-python

How to use allmalab/gpt2-aze with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="allmalab/gpt2-aze",
	filename="gpt2-aze.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 allmalab/gpt2-aze with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf allmalab/gpt2-aze
# Run inference directly in the terminal:
llama-cli -hf allmalab/gpt2-aze

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf allmalab/gpt2-aze
# Run inference directly in the terminal:
llama-cli -hf allmalab/gpt2-aze

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 allmalab/gpt2-aze
# Run inference directly in the terminal:
./llama-cli -hf allmalab/gpt2-aze

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 allmalab/gpt2-aze
# Run inference directly in the terminal:
./build/bin/llama-cli -hf allmalab/gpt2-aze

Use Docker

docker model run hf.co/allmalab/gpt2-aze

LM Studio
Jan

vLLM

How to use allmalab/gpt2-aze with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "allmalab/gpt2-aze"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "allmalab/gpt2-aze",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/allmalab/gpt2-aze

SGLang

How to use allmalab/gpt2-aze 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 "allmalab/gpt2-aze" \
    --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": "allmalab/gpt2-aze",
		"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 "allmalab/gpt2-aze" \
        --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": "allmalab/gpt2-aze",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use allmalab/gpt2-aze with Ollama:
```
ollama run hf.co/allmalab/gpt2-aze
```

Unsloth Studio new

How to use allmalab/gpt2-aze 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 allmalab/gpt2-aze 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 allmalab/gpt2-aze to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for allmalab/gpt2-aze to start chatting

Docker Model Runner
How to use allmalab/gpt2-aze with Docker Model Runner:
```
docker model run hf.co/allmalab/gpt2-aze
```

Lemonade

How to use allmalab/gpt2-aze with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull allmalab/gpt2-aze

Run and chat with the model

lemonade run user.gpt2-aze-{{QUANT_TAG}}

List all available models

lemonade list

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Azerbaijani GPT2 Model

The model is based on the GPT-2 architecture, specifically trained on Azerbaijani text. It serves as one of the first foundational models designed to generate and understand Azerbaijani language content. Built with the autoregressive transformer decoder architecture, the model generates text token by token, predicting the next word based on the input context.

Developed by : aLLMA Lab
Funded by : PRODATA LLC
Model type: Decoder-only foundational LLM
Language: Azerbaijani

Uses

The model can be directly used for text generation, sentence completion, next token prediction tasks by providing an input prompt. Additionally, it can be fine-tuned on an Azerbaijani instruction dataset to develop an interactive question-answering model.

Training Details

context_window=1024
stride=512

lr=1e-3
warmup_steps = 10000
weight_decay=0.1,
adam_beta1 = 0.9,
adam_beta2 = 0.999
batch_size=512
max_steps=178000

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Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for allmalab/gpt2-aze

Base model

openai-community/gpt2

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gpt2-aze

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Azerbaijani GPT2 Model

Uses

Training Details

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Datasets used to train allmalab/gpt2-aze