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
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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.
|