Instructions to use MK0727/lambda-1-160m-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MK0727/lambda-1-160m-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MK0727/lambda-1-160m-base")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MK0727/lambda-1-160m-base", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MK0727/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 "MK0727/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": "MK0727/lambda-1-160m-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MK0727/lambda-1-160m-base
- SGLang
How to use MK0727/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 "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 }'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 "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 }' - Docker Model Runner
How to use MK0727/lambda-1-160m-base with Docker Model Runner:
docker model run hf.co/MK0727/lambda-1-160m-base
| language: | |
| - ja | |
| library_name: transformers | |
| tags: | |
| - myllm | |
| - causal-lm | |
| - custom-code | |
| - safetensors | |
| pipeline_tag: text-generation | |
| # 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](https://github.com/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 | |
| ```bash | |
| 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. | |