Instructions to use CodeIsAbstract/HybridTimeScaleModel-Instruct-Tuned-0.1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CodeIsAbstract/HybridTimeScaleModel-Instruct-Tuned-0.1B with Transformers:

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

pipe = pipeline("text-generation", model="CodeIsAbstract/HybridTimeScaleModel-Instruct-Tuned-0.1B", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("CodeIsAbstract/HybridTimeScaleModel-Instruct-Tuned-0.1B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use CodeIsAbstract/HybridTimeScaleModel-Instruct-Tuned-0.1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CodeIsAbstract/HybridTimeScaleModel-Instruct-Tuned-0.1B"
# 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-Instruct-Tuned-0.1B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/CodeIsAbstract/HybridTimeScaleModel-Instruct-Tuned-0.1B

SGLang

How to use CodeIsAbstract/HybridTimeScaleModel-Instruct-Tuned-0.1B 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-Instruct-Tuned-0.1B" \
    --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-Instruct-Tuned-0.1B",
		"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-Instruct-Tuned-0.1B" \
        --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-Instruct-Tuned-0.1B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use CodeIsAbstract/HybridTimeScaleModel-Instruct-Tuned-0.1B with Docker Model Runner:
```
docker model run hf.co/CodeIsAbstract/HybridTimeScaleModel-Instruct-Tuned-0.1B
```

HybridTimeScaleModel-Instruct-Tuned-0.1B

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: 3e-05
train_batch_size: 4
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 16
total_train_batch_size: 64
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.05
num_epochs: 1

Training results

Framework versions

Transformers 4.46.3
Pytorch 2.8.0
Datasets 2.20.0
Tokenizers 0.20.3

Downloads last month: 284

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for CodeIsAbstract/HybridTimeScaleModel-Instruct-Tuned-0.1B

Base model

CodeIsAbstract/HybridTimeScaleModel

Finetuned

(2)

this model