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")

# pip install -U transformers accelerate
# 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
```

KeisukeMiyamoto commited on Aug 21

Commit

2fd3442

verified ·

1 Parent(s): c2654df

Clarify student developer wording

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -63,7 +63,7 @@ The model was trained on a limited Japanese corpus mixture and has not been eval
 ## Support Lambda
-[Lambda](https://github.com/KeisukeMiyamoto1324/lambda) is a project developed by a student to build small Japanese language models from scratch. The datasets, source code, and model weights are publicly available.
 Training language models is very expensive, and I have funded the project with income from my part-time job. As the models grow, the cost is becoming too high for me to maintain the current development pace.

 ## Support Lambda
+[Lambda](https://github.com/KeisukeMiyamoto1324/lambda) is a project I am developing as a student to build small Japanese language models from scratch. The datasets, source code, and model weights are publicly available.
 Training language models is very expensive, and I have funded the project with income from my part-time job. As the models grow, the cost is becoming too high for me to maintain the current development pace.