Instructions to use vtava/Tiny-LLM-PDelta3-GDN2-InputRoute with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vtava/Tiny-LLM-PDelta3-GDN2-InputRoute with Transformers:

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

pipe = pipeline("text-generation", model="vtava/Tiny-LLM-PDelta3-GDN2-InputRoute")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("vtava/Tiny-LLM-PDelta3-GDN2-InputRoute", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use vtava/Tiny-LLM-PDelta3-GDN2-InputRoute with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vtava/Tiny-LLM-PDelta3-GDN2-InputRoute"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vtava/Tiny-LLM-PDelta3-GDN2-InputRoute",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/vtava/Tiny-LLM-PDelta3-GDN2-InputRoute

SGLang

How to use vtava/Tiny-LLM-PDelta3-GDN2-InputRoute 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 "vtava/Tiny-LLM-PDelta3-GDN2-InputRoute" \
    --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": "vtava/Tiny-LLM-PDelta3-GDN2-InputRoute",
		"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 "vtava/Tiny-LLM-PDelta3-GDN2-InputRoute" \
        --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": "vtava/Tiny-LLM-PDelta3-GDN2-InputRoute",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use vtava/Tiny-LLM-PDelta3-GDN2-InputRoute with Docker Model Runner:
```
docker model run hf.co/vtava/Tiny-LLM-PDelta3-GDN2-InputRoute
```

Tiny-LLM-PDelta3-GDN2-InputRoute / candidate_diagnostics.csv

vtava

Upload conv4_gdn2_inputroute_f96 from TinyCeNN Tiny-LLM compatibility benchmark

c21da77 verified about 1 month ago

Raw History Blame Contribute Delete

910 Bytes

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,,,,conv4_pdelta_f96,4962816,42.58712899996908,0.013112435919176879,,19392,2359296,0.5584209999938139,
554.771728515625,67.48888397216797,9.20348072052002,,conv4_channel_decay_f96,5543936,40.24637999998504,0.014004787510698261,,19392,2359296,0.5636419999746067,
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	decay_half_life_max,decay_half_life_median,decay_half_life_min,erase_bias_mean,name,peak_extra_bytes,prefill_ms,prefill_speed_ratio,route_bias_mean,state_bytes,transformer_peak_extra_bytes,transformer_reference_ms,write_bias_mean
	,,,,conv4_pdelta_f96,4962816,42.58712899996908,0.013112435919176879,,19392,2359296,0.5584209999938139,
	554.771728515625,67.48888397216797,9.20348072052002,,conv4_channel_decay_f96,5543936,40.24637999998504,0.014004787510698261,,19392,2359296,0.5636419999746067,
	440.4382629394531,54.40313720703125,8.761691093444824,0.2685280740261078,conv4_gdn2_f96,7718400,20.124331000033635,0.021774537498052783,,20736,2359296,0.43819799998345843,0.2934897243976593
	441.93780517578125,54.05061340332031,8.751357078552246,0.26874133944511414,conv4_gdn2_inputroute_f96,8898048,20.739377000040804,0.021717142225443617,0.022410448640584946,20736,2359296,0.4503999999769803,0.29230982065200806