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