Instructions to use KeisukeMiyamoto/lambda-1-160m-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KeisukeMiyamoto/lambda-1-160m-base with Transformers:

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

pipe = pipeline("text-generation", model="KeisukeMiyamoto/lambda-1-160m-base")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("KeisukeMiyamoto/lambda-1-160m-base", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use KeisukeMiyamoto/lambda-1-160m-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KeisukeMiyamoto/lambda-1-160m-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KeisukeMiyamoto/lambda-1-160m-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/KeisukeMiyamoto/lambda-1-160m-base

SGLang

How to use KeisukeMiyamoto/lambda-1-160m-base 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 "KeisukeMiyamoto/lambda-1-160m-base" \
    --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": "KeisukeMiyamoto/lambda-1-160m-base",
		"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 "KeisukeMiyamoto/lambda-1-160m-base" \
        --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": "KeisukeMiyamoto/lambda-1-160m-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use KeisukeMiyamoto/lambda-1-160m-base with Docker Model Runner:
```
docker model run hf.co/KeisukeMiyamoto/lambda-1-160m-base
```

lambda-1-160m-base

lambda-1-160m-base is an experimental language model created with a custom myllm decoder-only Transformer implementation.

All training code is publicly available at KeisukeMiyamoto1324/myllm.

Model Details

Item	Value
Parameters	164.5M
Architecture	Decoder-only Transformer
Context length	1024 tokens
Tokenizer	Byte-level BPE
Vocabulary size	65,536
Layers	16
Hidden size	768
Attention heads	12
FFN size	3,072

Training Data

The model was pretrained on a Japanese text mixture.

Dataset	Notes
`MK0727/CleanedFineWeb2Edu-jp`	Filtered Japanese web corpus
`MK0727/SyntheticTextbook-jp`	Synthetic Japanese corpus

Usage

git clone https://github.com/KeisukeMiyamoto1324/lambda.git
cd lambda
python3 -m venv venv
source venv/bin/activate
pip3 install -r requirements.txt

python3 src/inference_base/inference_hf.py \
  --prompt "人工知能とは" \
  --max-new-tokens 64

Limitations

This model is not instruction-tuned or safety-aligned. It may generate incorrect, biased, unsafe, or low-quality text.

The model was trained on a limited Japanese corpus mixture and has not been evaluated on standard benchmarks.

Support Lambda

Lambda is an open-source project for building small Japanese language models from scratch. As a student, I have funded this project with income from my part-time job, but the growing training costs are becoming difficult to cover.

Your support helps cover GPU costs and develop larger models. Thank you for helping Lambda continue to grow.

Vast.ai

Vast.ai offers affordable cloud GPUs for AI training, with NVIDIA H100 SXM GPUs available from around $1.54 per hour. If you purchase credits through the link below, I receive 3% in GPU credits at no extra cost to you.

https://cloud.vast.ai/?ref_id=521936

Ko-fi

Support Lambda with a donation starting from $5.

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