Instructions to use LoneStriker/LWM-Text-Chat-512K-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LoneStriker/LWM-Text-Chat-512K-AWQ with Transformers:

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

pipe = pipeline("text-generation", model="LoneStriker/LWM-Text-Chat-512K-AWQ")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LoneStriker/LWM-Text-Chat-512K-AWQ")
model = AutoModelForCausalLM.from_pretrained("LoneStriker/LWM-Text-Chat-512K-AWQ", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LoneStriker/LWM-Text-Chat-512K-AWQ with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/LoneStriker/LWM-Text-Chat-512K-AWQ

SGLang

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

Docker Model Runner
How to use LoneStriker/LWM-Text-Chat-512K-AWQ with Docker Model Runner:
```
docker model run hf.co/LoneStriker/LWM-Text-Chat-512K-AWQ
```

Configuration Parsing Warning:In config.json: "quantization_config.modules_to_not_convert" must be an array

LWM-Text-Chat-512K Model Card

Model details

Model type: LWM-Text-Chat-512K 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-Chat-512K was trained in December 2023.

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

License

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

Training dataset

3500 subset of Books3 documents with 500K to 1M tokens

Downloads last month: 11

Safetensors

Model size

7B params

Tensor type

I32

F16