Instructions to use kyLELEng/market-gpt-return-token with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kyLELEng/market-gpt-return-token with Transformers:

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

pipe = pipeline("text-generation", model="kyLELEng/market-gpt-return-token")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kyLELEng/market-gpt-return-token")
model = AutoModelForCausalLM.from_pretrained("kyLELEng/market-gpt-return-token")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use kyLELEng/market-gpt-return-token with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/kyLELEng/market-gpt-return-token

SGLang

How to use kyLELEng/market-gpt-return-token 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 "kyLELEng/market-gpt-return-token" \
    --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": "kyLELEng/market-gpt-return-token",
		"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 "kyLELEng/market-gpt-return-token" \
        --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": "kyLELEng/market-gpt-return-token",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use kyLELEng/market-gpt-return-token with Docker Model Runner:
```
docker model run hf.co/kyLELEng/market-gpt-return-token
```

market-gpt-return-token / distribution_comparison.csv

kyLELEng

Train MarketGPT return-token model

c011bf2 verified about 1 month ago

raw

history blame contribute delete

544 Bytes

	series,metric,value
	generated,mean,0.004333046730607748
	generated,std,0.08561652898788452
	generated,skew,-0.5086434483528137
	generated,kurtosis,56.622493743896484
	generated,q01,-0.2753317952156067
	generated,q05,-0.037811942398548126
	generated,q95,0.05145534127950668
	generated,q99,0.33806970715522766
	true,mean,-0.002802362898364663
	true,std,0.030968399718403816
	true,skew,-2.6303722858428955
	true,kurtosis,23.258527755737305
	true,q01,-0.09194755554199219
	true,q05,-0.05170159786939621
	true,q95,0.04157276824116707
	true,q99,0.06143633648753166