Instructions to use AtAndDev/ShortKing-1.4b-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AtAndDev/ShortKing-1.4b-v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="AtAndDev/ShortKing-1.4b-v0.1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AtAndDev/ShortKing-1.4b-v0.1")
model = AutoModelForCausalLM.from_pretrained("AtAndDev/ShortKing-1.4b-v0.1")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use AtAndDev/ShortKing-1.4b-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AtAndDev/ShortKing-1.4b-v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AtAndDev/ShortKing-1.4b-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AtAndDev/ShortKing-1.4b-v0.1

SGLang

How to use AtAndDev/ShortKing-1.4b-v0.1 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 "AtAndDev/ShortKing-1.4b-v0.1" \
    --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": "AtAndDev/ShortKing-1.4b-v0.1",
		"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 "AtAndDev/ShortKing-1.4b-v0.1" \
        --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": "AtAndDev/ShortKing-1.4b-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AtAndDev/ShortKing-1.4b-v0.1 with Docker Model Runner:
```
docker model run hf.co/AtAndDev/ShortKing-1.4b-v0.1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Overview

Model license: cc-by-nc-4.0
This model is trained based on EleutherAI/pythia-1.4b-deduped model that is LoRA finetuned on vicgalle/alpaca-gpt4 dataset.

Prompt Template: `Alpaca`

<system_prompt>

### Instruction:
<user_message>

### Response:
<assistant_response>

Intended Use

THIS IS A TEST MODEL, IT IS NOT INTENDED FOR REAL APPLICATIONS BY ANY MEANS. HOWEVER, A NEW MODEL IS COMING IN THE SAME TOPIC.
This model series will be used for small but intense applications.

Training Details

This model took 2:31:23 to train in QLoRA on a single T4 GPU.

epochs: 1
train batch size: 12
eval batch size: 12
gradient accumulation steps: 1
maximum gradient normal: 0.3
learning rate: 2e-4
weight decay: 0.001
optimizer: paged_adamw_32bit
learning rate schedule: cosine
warmup ratio (linear): 0.03

Downloads last month: 716

Safetensors

Model size

1B params

Tensor type

F32

F16

Model tree for AtAndDev/ShortKing-1.4b-v0.1

Quantizations

1 model

AtAndDev
/

ShortKing-1.4b-v0.1

Model Overview

Prompt Template: `Alpaca`

Intended Use

Training Details

Model tree for AtAndDev/ShortKing-1.4b-v0.1

Dataset used to train AtAndDev/ShortKing-1.4b-v0.1

Model Overview

Prompt Template: Alpaca

Intended Use

Training Details

Model tree for AtAndDev/ShortKing-1.4b-v0.1

Dataset used to train AtAndDev/ShortKing-1.4b-v0.1

Prompt Template: `Alpaca`