Instructions to use hongyin/chat-self-management-1.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hongyin/chat-self-management-1.5b with Transformers:

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

pipe = pipeline("text-generation", model="hongyin/chat-self-management-1.5b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hongyin/chat-self-management-1.5b")
model = AutoModelForCausalLM.from_pretrained("hongyin/chat-self-management-1.5b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use hongyin/chat-self-management-1.5b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hongyin/chat-self-management-1.5b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hongyin/chat-self-management-1.5b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/hongyin/chat-self-management-1.5b

SGLang

How to use hongyin/chat-self-management-1.5b 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 "hongyin/chat-self-management-1.5b" \
    --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": "hongyin/chat-self-management-1.5b",
		"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 "hongyin/chat-self-management-1.5b" \
        --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": "hongyin/chat-self-management-1.5b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use hongyin/chat-self-management-1.5b with Docker Model Runner:
```
docker model run hf.co/hongyin/chat-self-management-1.5b
```

hongyin/chat-self-management-1.5b

Warning: There are some problems with the tokenizer of this model, which will be corrected in the next version of the model (chat-informer-1b).

We are honored to introduce a lightweight Chinese-English conversation assistant designed to reduce the cost of inference. It is trained from scratch, based on the LLAMA2 architecture, with 150 million parameters and a completely new vocabulary. The training process consists of two parts: (1) NTP task. (2) Instruction tuning. The model improves data quality for pre-training and instruction tuning.

Human: Paraphrasing the  sentence: I love you.
Assistant: Sure, I love you.

Bibtex entry and citation info

Please cite if you find it helpful.

@article{zhu2023metaaid,
  title={MetaAID 2.0: An Extensible Framework for Developing Metaverse Applications via Human-controllable Pre-trained Models},
  author={Zhu, Hongyin},
  journal={arXiv preprint arXiv:2302.13173},
  year={2023}
}

license: other

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Paper for hongyin/chat-self-management-1.5b

MetaAID 2.0: An Extensible Framework for Developing Metaverse Applications via Human-controllable Pre-trained Models

Paper • 2302.13173 • Published Feb 25, 2023