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

File size: 756 Bytes

f169e35

{
  "_name_or_path": "CodeIsAbstract/HybridTimeScaleModel",
  "architectures": [
    "HybridFourierLM"
  ],
  "auto_map": {
    "AutoConfig": "model.HybridFourierConfig",
    "AutoModelForCausalLM": "model.HybridFourierLM"
  },
  "bos_token_id": 1,
  "dropout": 0.05,
  "dtype": "float32",
  "eos_token_id": 2,
  "latent_dim": 768,
  "layer_types": [
    "linear",
    "linear",
    "linear",
    "softmax",
    "linear",
    "linear",
    "linear",
    "softmax",
    "linear",
    "linear",
    "linear",
    "softmax"
  ],
  "model_type": "hybrid_fourier_lm",
  "num_layers": 12,
  "num_modes": 64,
  "pad_token_id": 2,
  "time_scale": 128.0,
  "torch_dtype": "float32",
  "transformers_version": "4.46.3",
  "use_cache": false,
  "vocab_size": 32768
}