Instructions to use LargeWorldModel/LWM-Text-256K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LargeWorldModel/LWM-Text-256K with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="LargeWorldModel/LWM-Text-256K")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LargeWorldModel/LWM-Text-256K")
model = AutoModelForCausalLM.from_pretrained("LargeWorldModel/LWM-Text-256K")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use LargeWorldModel/LWM-Text-256K with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LargeWorldModel/LWM-Text-256K"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LargeWorldModel/LWM-Text-256K",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LargeWorldModel/LWM-Text-256K

SGLang

How to use LargeWorldModel/LWM-Text-256K 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 "LargeWorldModel/LWM-Text-256K" \
    --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": "LargeWorldModel/LWM-Text-256K",
		"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 "LargeWorldModel/LWM-Text-256K" \
        --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": "LargeWorldModel/LWM-Text-256K",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LargeWorldModel/LWM-Text-256K with Docker Model Runner:
```
docker model run hf.co/LargeWorldModel/LWM-Text-256K
```

LWM-Text-256K

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---
inference: false
---

<br>
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# LWM-Text-256K Model Card

## Model details

**Model type:**
LWM-Text-256K is an open-source model trained from LLaMA-2 on a subset of Books3 filtered data. It is an auto-regressive language model, based on the transformer architecture.

**Model date:**
LWM-Text-256K was trained in December 2023.

**Paper or resources for more information:**
https://largeworldmodel.github.io/

## License
Llama 2 is licensed under the LLAMA 2 Community License, 
Copyright (c) Meta Platforms, Inc. All Rights Reserved.

**Where to send questions or comments about the model:**
https://github.com/LargeWorldModel/lwm/issues

## Training dataset
- 37K subset of Books3 documents with 200K to 500K tokens