Instructions to use aoxo/Ma-layala-mba_Tiny_128M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aoxo/Ma-layala-mba_Tiny_128M with Transformers:

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

pipe = pipeline("text-generation", model="aoxo/Ma-layala-mba_Tiny_128M")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aoxo/Ma-layala-mba_Tiny_128M")
model = AutoModelForCausalLM.from_pretrained("aoxo/Ma-layala-mba_Tiny_128M")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use aoxo/Ma-layala-mba_Tiny_128M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aoxo/Ma-layala-mba_Tiny_128M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aoxo/Ma-layala-mba_Tiny_128M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/aoxo/Ma-layala-mba_Tiny_128M

SGLang

How to use aoxo/Ma-layala-mba_Tiny_128M 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 "aoxo/Ma-layala-mba_Tiny_128M" \
    --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": "aoxo/Ma-layala-mba_Tiny_128M",
		"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 "aoxo/Ma-layala-mba_Tiny_128M" \
        --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": "aoxo/Ma-layala-mba_Tiny_128M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use aoxo/Ma-layala-mba_Tiny_128M with Docker Model Runner:
```
docker model run hf.co/aoxo/Ma-layala-mba_Tiny_128M
```

Ma-layala-mba

Welcome to Ma-layala-mba, a base Indic language model designed to push the boundaries of NLP for Indian languages. It is based on the Mamba series of state space models.

Model Description

Ma-layala-mba is a state-of-the-art S6 SSM model specifically crafted for the South Indian regional and state language of Kerala: Malayalam. It integrates traditional Attention mechanisms with innovative approaches such as MLPs and State Space Models (SSMs) to handle complex linguistic features and achieve high accuracy in language understanding and generation.

Model Type: A 128M Jamba model finetuned on ~1 million samples of Malayalam prompt-response pairs from a subset of the IndicCorp Dataset
Language(s): Malayalam
License: GNU General Public License v3.0
Training Precision: bfloat16

Example Usage

Here's a quick example to get you started with the Ma-layala-mba model:

from transformers import MaLayalaMbaForCausalLM, AutoTokenizer, pipeline

model = MaLayalaMbaForCausalLM.from_pretrained(
    "aoxo/Ma-layala-mba_Tiny_128M",
    # load_in_8bit=True, # Set this depending on the GPU you have
    torch_dtype=torch.bfloat16,
    device_map={"": 0}, # Set this depending on the number of GPUs you have
    local_files_only=False # Optional
)
model.eval()

tokenizer = AutoTokenizer.from_pretrained("aoxo/Ma-layala-mba_Tiny_128M")

input_ids = tokenizer("മലയാളം പര്യായപദങ്ങളിൽ ഒരു പരീക്ഷ പേപ്പർ ഉണ്ടാക്കുക", return_tensors='pt').to(model.device)["input_ids"]

outputs = model.generate(input_ids, max_new_tokens=100)

print(tokenizer.batch_decode(outputs))

Example Output:

മലയാളം പര്യായപദങ്ങളിൽ ഒരു പരീക്ഷ പേപ്പർ ഉണ്ടാക്കുക

a. വലിയ - __________
b. രസം - __________
c. സുഖം - __________
d. പ്രകാശം - __________
e. വേഗം - __________

Usage Note

Please be aware that this model has not undergone comprehensive detoxification or censorship. While it exhibits strong linguistic capabilities, there is a possibility of generating content that may be deemed harmful or offensive. We advise users to apply discretion and closely monitor the model's outputs, especially in public or sensitive settings.

Meet the Developers

Alosh Denny

Downloads last month: 9

Safetensors

Model size

0.1B params

Tensor type

BF16

aoxo
/

Ma-layala-mba_Tiny_128M