How to use from
SGLangUse 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 "MK0727/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": "MK0727/lambda-1-160m-base",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Quick Links
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.
Install from pip and serve model
# Install SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "MK0727/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": "MK0727/lambda-1-160m-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'