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
| {"max_len": 1024, "d_model": 768, "num_layers": 16, "num_heads": 12, "d_ff": 3072, "learning_rate": 0.0001, "batch_size": 72, "gradient_accumulation_steps": 2, "effective_batch_size": 144, "lr_schedule": "fixed", "loss_chunk_size": 32, "pad_token_id": 0, "bos_token_id": 2, "eos_token_id": 3, "corpus_signature": "ba5e40d4dae8dceb", "dataset_case": {"name": "synthetic-textbook-jp", "genre": "textbook", "language": "ja", "dataset_path": "MK0727/SyntheticTextbook-jp", "config_name": "default", "split": "train", "text_column": "rewrite"}, "val_split_modulo": 100, "val_split_index": 0, "validation_cache_path": "models/lambda-160m-midtrained/validation-cache-ba5e40d4dae8dceb-bucket-packing-v1-len1024-samples4608-split100-0.pt", "validation_sample_count": 4608, "packing_version": "bucket-packing-v1", "trained_steps": 10240, "midtraining_source_model": "models/lambda-160m", "midtraining_max_steps": 10240, "shuffle_buffer_size": 10000, "shuffle_seed": 17} |