Instructions to use CodeIsAbstract/HybridTimeScaleModel_conti with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CodeIsAbstract/HybridTimeScaleModel_conti with Transformers:

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

pipe = pipeline("text-generation", model="CodeIsAbstract/HybridTimeScaleModel_conti", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("CodeIsAbstract/HybridTimeScaleModel_conti", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use CodeIsAbstract/HybridTimeScaleModel_conti with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/CodeIsAbstract/HybridTimeScaleModel_conti

SGLang

How to use CodeIsAbstract/HybridTimeScaleModel_conti 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 "CodeIsAbstract/HybridTimeScaleModel_conti" \
    --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": "CodeIsAbstract/HybridTimeScaleModel_conti",
		"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 "CodeIsAbstract/HybridTimeScaleModel_conti" \
        --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": "CodeIsAbstract/HybridTimeScaleModel_conti",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use CodeIsAbstract/HybridTimeScaleModel_conti with Docker Model Runner:
```
docker model run hf.co/CodeIsAbstract/HybridTimeScaleModel_conti
```

HybridTimeScaleModel_conti / README.md

CodeIsAbstract

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metadata

library_name: transformers
base_model: CodeIsAbstract/HybridTimeScaleModel
tags:
  - generated_from_trainer
model-index:
  - name: HybridTimeScaleModel_conti
    results: []

HybridTimeScaleModel_conti

This model is a fine-tuned version of CodeIsAbstract/HybridTimeScaleModel on an unknown dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0001
train_batch_size: 1
eval_batch_size: 4
seed: 42
distributed_type: tpu
optimizer: Use OptimizerNames.ADAFACTOR and the args are: relative_step=False,scale_parameter=False,warmup_init=False
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 0.05
training_steps: 11000

Training results

Framework versions

Transformers 5.12.1
Pytorch 2.9.0+cpu
Datasets 5.0.0
Tokenizers 0.22.2