Instructions to use kotoba-tech/kotomamba-2.8B-v1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kotoba-tech/kotomamba-2.8B-v1.0 with Transformers:

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

pipe = pipeline("text-generation", model="kotoba-tech/kotomamba-2.8B-v1.0")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("kotoba-tech/kotomamba-2.8B-v1.0", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kotoba-tech/kotomamba-2.8B-v1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kotoba-tech/kotomamba-2.8B-v1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kotoba-tech/kotomamba-2.8B-v1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/kotoba-tech/kotomamba-2.8B-v1.0

SGLang

How to use kotoba-tech/kotomamba-2.8B-v1.0 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 "kotoba-tech/kotomamba-2.8B-v1.0" \
    --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": "kotoba-tech/kotomamba-2.8B-v1.0",
		"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 "kotoba-tech/kotomamba-2.8B-v1.0" \
        --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": "kotoba-tech/kotomamba-2.8B-v1.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use kotoba-tech/kotomamba-2.8B-v1.0 with Docker Model Runner:
```
docker model run hf.co/kotoba-tech/kotomamba-2.8B-v1.0
```

Configuration Parsing Warning:In config.json: "architectures" must be an array

Configuration Parsing Warning:In config.json: "model_type" is not allowed to be empty

Kotomamba

The kotomamba model represents a cutting-edge approach in natural language processing (NLP), leveraging the innovative State Space Model mamba architecture. The kotomamba model comes in two distinct versions.

Bilingual Pre-training (Japanese and English): The first variant of the kotomamba model is pre-trained on a rich dataset(About 200B Token) comprising both Japanese and English texts.
Continual Pre-training (Mainly Japanese): The second variant of the kotomamba model takes a different approach, focusing exclusively on Japanese-centric data for its continual pre-training phase.

Kotomamba Model Index

Model	kotomamba-hf
kotomamba-2.8B-v1.0	Link
kotomamba-2.8B-CL-v1.0	Link

This repository provides large language models developed by Kotoba Technologies, Tohoku University TohokuNLP group, and Tokyo Institute of Technology Okazaki Lab, Yokota Lab. Read our blog post or our technical paper (preprint coming soon) for more details!

Model Details

Model type: Please refer to mamba technical paper for details on the model architecture.
Language(s): Japanese English
Library: kotomamba
Tokenizer: kotomamba-2.8B uses llm-jp-tokenizer 100K and kotomamba-2.8B-CL uses GPT-NeoX Tokenizer.
Contact:

Base Model Performance

Japanese version

Model	Size	JCommonsenseQA	JEMHopQA	NIILC	JSQuAD
		4-shot	4-shot	4-shot	4-shot
state-spaces/mamba-2.8b-slimpj	2.8B	0.1796	0.2825	0.0998	0.3301
kotomamba-2.8B	2.8B	0.185	0.4532	0.3871	0.4685
kotomamba-2.8B-CL	2.8B	0.185	0.3758	0.2393	0.5929

Usage

git clone https://github.com/kotoba-tech/kotomamba and follow the repository's README installation section.

WARNING: huggingface transformers AutoModelForCausalLM doesn't support mamba model. So, please use kotomamba/benchmarks/benchmark_generation_mamba_simple.py

You can find the inference sample script in scripts/abci/inference/inference_sample.sh

Training Datasets

Pre-Training & Continual Pre-Training

The following datasets were used for training.

Risks and Limitations

The models released here are still in the early stages of our research and development and have not been tuned to ensure outputs align with human intent and safety considerations.