Instructions to use 922-Narra/gemma-2-9b-tagalog-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 922-Narra/gemma-2-9b-tagalog-chat with Transformers:

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

pipe = pipeline("text-generation", model="922-Narra/gemma-2-9b-tagalog-chat")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("922-Narra/gemma-2-9b-tagalog-chat")
model = AutoModelForCausalLM.from_pretrained("922-Narra/gemma-2-9b-tagalog-chat")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use 922-Narra/gemma-2-9b-tagalog-chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "922-Narra/gemma-2-9b-tagalog-chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "922-Narra/gemma-2-9b-tagalog-chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/922-Narra/gemma-2-9b-tagalog-chat

SGLang

How to use 922-Narra/gemma-2-9b-tagalog-chat 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 "922-Narra/gemma-2-9b-tagalog-chat" \
    --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": "922-Narra/gemma-2-9b-tagalog-chat",
		"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 "922-Narra/gemma-2-9b-tagalog-chat" \
        --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": "922-Narra/gemma-2-9b-tagalog-chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio new

How to use 922-Narra/gemma-2-9b-tagalog-chat 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 922-Narra/gemma-2-9b-tagalog-chat 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 922-Narra/gemma-2-9b-tagalog-chat to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for 922-Narra/gemma-2-9b-tagalog-chat to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="922-Narra/gemma-2-9b-tagalog-chat",
    max_seq_length=2048,
)

Docker Model Runner
How to use 922-Narra/gemma-2-9b-tagalog-chat with Docker Model Runner:
```
docker model run hf.co/922-Narra/gemma-2-9b-tagalog-chat
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

gemma-2-9b-tagalog-chat:

Tagalog model fine-tuned on this dataset
Base: gemma 2 9b
GGUF

USAGE

This model is meant to be a chat model that has better Tagalog capabilities.

Best results with "Human" and "Assistant" and prompt with Tagalog. Example:

"Ito ay isang chat log sa pagitan ng AI Assistant na nagta-Tagalog at isang Pilipino. Magsimula ng chat:\nHuman: Hello po?\nAssistant:"

HYPERPARAMS

Trained for 1 epoch
rank: 32
lora alpha: 32
lora dropout: 0
lr: 2e-4
batch size: 2
grad steps: 4

WARNINGS AND DISCLAIMERS

There is always the chance that the model may hallucinate.

At times, it may also still switch to English or Taglish (most instances of this occurring when tested for coding).

Finally, this model is not guaranteed to output aligned or safe outputs nor is it meant for production use - use at your own risk!

Downloads last month: 3

Safetensors

Model size

9B params

Tensor type

F16

Model tree for 922-Narra/gemma-2-9b-tagalog-chat

Base model

google/gemma-2-9b

Quantized

unsloth/gemma-2-9b-bnb-4bit

Finetuned

(487)

this model

Quantizations

1 model