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 / README.md

CodeIsAbstract

Model save

f169e35 verified 11 days ago

preview code

Raw

History Blame Contribute Delete

1.32 kB

	---
	library_name: transformers
	base_model: CodeIsAbstract/HybridTimeScaleModel
	tags:
	- generated_from_trainer
	model-index:
	- name: HybridTimeScaleModel-Instruct-Tuned-0.1B
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# HybridTimeScaleModel-Instruct-Tuned-0.1B

	This model is a fine-tuned version of [CodeIsAbstract/HybridTimeScaleModel](https://huggingface.co/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