MamayLM-v1.0-Gemma-3
Collection
First Open and Multimodal Ukrainian-focused LLM • 5 items • Updated • 21
How to use INSAIT-Institute/MamayLM-Gemma-3-12B-IT-v1.0-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="INSAIT-Institute/MamayLM-Gemma-3-12B-IT-v1.0-GGUF", filename="MamayLM-Gemma-3-12B-IT-v1.0.Q4_K_M.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
How to use INSAIT-Institute/MamayLM-Gemma-3-12B-IT-v1.0-GGUF with llama.cpp:
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf INSAIT-Institute/MamayLM-Gemma-3-12B-IT-v1.0-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf INSAIT-Institute/MamayLM-Gemma-3-12B-IT-v1.0-GGUF:Q4_K_M
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf INSAIT-Institute/MamayLM-Gemma-3-12B-IT-v1.0-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf INSAIT-Institute/MamayLM-Gemma-3-12B-IT-v1.0-GGUF:Q4_K_M
# 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 INSAIT-Institute/MamayLM-Gemma-3-12B-IT-v1.0-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf INSAIT-Institute/MamayLM-Gemma-3-12B-IT-v1.0-GGUF:Q4_K_M
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 INSAIT-Institute/MamayLM-Gemma-3-12B-IT-v1.0-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf INSAIT-Institute/MamayLM-Gemma-3-12B-IT-v1.0-GGUF:Q4_K_M
docker model run hf.co/INSAIT-Institute/MamayLM-Gemma-3-12B-IT-v1.0-GGUF:Q4_K_M
How to use INSAIT-Institute/MamayLM-Gemma-3-12B-IT-v1.0-GGUF with Ollama:
ollama run hf.co/INSAIT-Institute/MamayLM-Gemma-3-12B-IT-v1.0-GGUF:Q4_K_M
How to use INSAIT-Institute/MamayLM-Gemma-3-12B-IT-v1.0-GGUF with Unsloth Studio:
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 INSAIT-Institute/MamayLM-Gemma-3-12B-IT-v1.0-GGUF to start chatting
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 INSAIT-Institute/MamayLM-Gemma-3-12B-IT-v1.0-GGUF to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for INSAIT-Institute/MamayLM-Gemma-3-12B-IT-v1.0-GGUF to start chatting
How to use INSAIT-Institute/MamayLM-Gemma-3-12B-IT-v1.0-GGUF with Docker Model Runner:
docker model run hf.co/INSAIT-Institute/MamayLM-Gemma-3-12B-IT-v1.0-GGUF:Q4_K_M
How to use INSAIT-Institute/MamayLM-Gemma-3-12B-IT-v1.0-GGUF with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull INSAIT-Institute/MamayLM-Gemma-3-12B-IT-v1.0-GGUF:Q4_K_M
lemonade run user.MamayLM-Gemma-3-12B-IT-v1.0-GGUF-Q4_K_M
lemonade list
MamayLM is distributed under Gemma Terms of Use.
This repo contains the GGUF format model files for INSAIT-Institute/MamayLM-Gemma-3-12B-IT-v1.0.
Install the required package:
pip install llama-cpp-python
Example chat completion:
from llama_cpp import Llama
llm = Llama(
model_path="path/to/your/model.gguf",
n_ctx=128000,
penalize_nl=False
)
messages = [{"role": "user", "content": "Хто такий Козак Мамай??"}]
response = llm.create_chat_completion(
messages=messages,
max_tokens=2048, # Choose maximum generated tokens
temperature=0.1,
top_p=0.9,
repeat_penalty=1.0,
stop=["<eos>", "<end_of_turn>"]
)
Example normal completion:
from llama_cpp import Llama
llm = Llama(
model_path="path/to/your/model.gguf",
n_ctx=128000,
penalize_nl=False
)
prompt = "<start_of_turn>user\nХто такий Козак Мамай?<end_of_turn>\n<start_of_turn>model\n"
response = llm(
prompt,
max_tokens=2048, # Choose maximum generated tokens
temperature=0.1,
top_p=0.9,
repeat_penalty=1.0,
stop=["<eos>","<end_of_turn>"]
)
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