Instructions to use cropinailab/aksara_v1_GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cropinailab/aksara_v1_GGUF with Transformers:

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

pipe = pipeline("text-generation", model="cropinailab/aksara_v1_GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("cropinailab/aksara_v1_GGUF", dtype="auto")

llama-cpp-python

How to use cropinailab/aksara_v1_GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="cropinailab/aksara_v1_GGUF",
	filename="aksara_v1.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use cropinailab/aksara_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 cropinailab/aksara_v1_GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf cropinailab/aksara_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 cropinailab/aksara_v1_GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf cropinailab/aksara_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 cropinailab/aksara_v1_GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf cropinailab/aksara_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 cropinailab/aksara_v1_GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf cropinailab/aksara_v1_GGUF:Q4_K_M

Use Docker

docker model run hf.co/cropinailab/aksara_v1_GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use cropinailab/aksara_v1_GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cropinailab/aksara_v1_GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cropinailab/aksara_v1_GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cropinailab/aksara_v1_GGUF:Q4_K_M

SGLang

How to use cropinailab/aksara_v1_GGUF 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 "cropinailab/aksara_v1_GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cropinailab/aksara_v1_GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "cropinailab/aksara_v1_GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cropinailab/aksara_v1_GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use cropinailab/aksara_v1_GGUF with Ollama:
```
ollama run hf.co/cropinailab/aksara_v1_GGUF:Q4_K_M
```

Unsloth Studio new

How to use cropinailab/aksara_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 cropinailab/aksara_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 cropinailab/aksara_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 cropinailab/aksara_v1_GGUF to start chatting

Docker Model Runner
How to use cropinailab/aksara_v1_GGUF with Docker Model Runner:
```
docker model run hf.co/cropinailab/aksara_v1_GGUF:Q4_K_M
```

Lemonade

How to use cropinailab/aksara_v1_GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull cropinailab/aksara_v1_GGUF:Q4_K_M

Run and chat with the model

lemonade run user.aksara_v1_GGUF-Q4_K_M

List all available models

lemonade list

You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

This repo provides the GGUF format for the aksara_v1 model. This model has a precision of 4-bit and is capable of doing inference with GPU as well as CPU only.

To run using Python:

Install llama-cpp-python:

! CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python

Download the model:

from huggingface_hub import hf_hub_download

model_name = "cropinailab/aksara_v1_GGUF"
model_file = "aksara_v1.Q4_K_M.gguf"
model_path = hf_hub_download(model_name,
                             filename=model_file,
                             token='<YOUR_HF_TOKEN>'
                             local_dir='<PATH_TO_SAVE_MODEL>')

Run the model:

from llama_cpp import Llama
llm = Llama(
  model_path=model_path,  # path to GGUF file
  n_ctx=4096,  # The max sequence length to use - note that longer sequence lengths require much more resources
  n_gpu_layers=-1, # The number of layers to offload to GPU, if you have GPU acceleration available.
                   # Set to 0 if no GPU acceleration is available on your system and -1 for all GPU layers.
)
prompt = "What are the recommended NPK dosage for maize varieties?"

# Simple inference example
output = llm(
  f"<|user|>\n{prompt}<|end|>\n<|assistant|>",
  max_tokens=512,  # Generate up to 512 tokens
  stop=["<|end|>"], 
  echo=True,  # Whether to echo the prompt
)
print(output['choices'][0]['text'])

For using the model with a more detailed pipeline refer to the following notebook

Downloads last month: 4

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

4-bit