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

File size: 1,275 Bytes

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{
  "model_repo_id": "kyLELEng/market-gpt-return-token-v2",
  "dataset_id": "siddharthmb/stocks-ohlcv",
  "dataset_file": "ohlcv.csv",
  "use_synthetic": false,
  "synthetic_steps": 900,
  "tickers": [
    "AAPL",
    "MSFT",
    "AMZN",
    "GOOGL",
    "NVDA",
    "TSLA",
    "AMD",
    "INTC",
    "JPM",
    "BAC",
    "V",
    "MA"
  ],
  "num_assets": 12,
  "num_buckets": 101,
  "bucket_method": "quantile",
  "clip_value": 5.0,
  "vol_span": 60,
  "context_steps": 128,
  "prediction_steps": 32,
  "window_stride": 1,
  "train_fraction": 0.8,
  "validation_fraction": 0.1,
  "n_embd": 384,
  "n_layer": 6,
  "n_head": 6,
  "dropout": 0.1,
  "forecast_only_loss": true,
  "per_device_train_batch_size": 4,
  "per_device_eval_batch_size": 4,
  "gradient_accumulation_steps": 8,
  "learning_rate": 0.0003,
  "weight_decay": 0.1,
  "warmup_ratio": 0.05,
  "num_train_epochs": 5.0,
  "max_steps": -1,
  "logging_steps": 20,
  "eval_strategy": "epoch",
  "save_strategy": "no",
  "bf16": true,
  "fp16": false,
  "random_seed": 7,
  "num_eval_contexts": 12,
  "num_sample_paths": 24,
  "generation_temperature": 1.0,
  "generation_top_p": 0.95,
  "max_csv_chunks": null,
  "csv_chunksize": 500000,
  "min_rows_per_ticker": 1000,
  "output_dir": "market-gpt-return-token"
}