Text Generation
Transformers
PyTorch
English
taonet_mini_t2
taonet
taotern
ssm
state-space-model
dplr
custom_code
experimental
Instructions to use TaoTern/TaoNet-mini-T2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TaoTern/TaoNet-mini-T2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TaoTern/TaoNet-mini-T2", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("TaoTern/TaoNet-mini-T2", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use TaoTern/TaoNet-mini-T2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TaoTern/TaoNet-mini-T2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TaoTern/TaoNet-mini-T2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/TaoTern/TaoNet-mini-T2
- SGLang
How to use TaoTern/TaoNet-mini-T2 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 "TaoTern/TaoNet-mini-T2" \ --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": "TaoTern/TaoNet-mini-T2", "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 "TaoTern/TaoNet-mini-T2" \ --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": "TaoTern/TaoNet-mini-T2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use TaoTern/TaoNet-mini-T2 with Docker Model Runner:
docker model run hf.co/TaoTern/TaoNet-mini-T2
Add files using upload-large-folder tool
Browse files- model/pretrain_final_model.pt +3 -0
- upload_large_folder.ps1 +17 -2
model/pretrain_final_model.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:fadb4e316daf89c3952174aa515de0793999106605a907eb592cb50145515cdf
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size 2359610207
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upload_large_folder.ps1
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$ErrorActionPreference = "Stop"
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$Root = Split-Path -Parent $MyInvocation.MyCommand.Path
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if (-not $env:HF_TOKEN) {
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& $HfExe auth whoami *> $null
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-
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Write-Host "Not logged in to Hugging Face. Run one of these first:"
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Write-Host "
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Write-Host "or:"
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Write-Host ' $env:HF_TOKEN="YOUR_TOKEN"'
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exit 1
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$ErrorActionPreference = "Stop"
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$Root = Split-Path -Parent $MyInvocation.MyCommand.Path
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$BundledHf = "C:\Users\USER\.cache\codex-runtimes\codex-primary-runtime\dependencies\python\Scripts\hf.exe"
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if ($HfExe -eq "hf" -and -not (Get-Command $HfExe -ErrorAction SilentlyContinue) -and (Test-Path $BundledHf)) {
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$HfExe = $BundledHf
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}
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if (-not (Get-Command $HfExe -ErrorAction SilentlyContinue) -and -not (Test-Path $HfExe)) {
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Write-Host "Could not find Hugging Face CLI: $HfExe"
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Write-Host "Install it with: pip install -U huggingface_hub[hf_xet]"
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exit 1
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}
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if (-not $env:HF_TOKEN) {
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$oldPreference = $ErrorActionPreference
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$ErrorActionPreference = "Continue"
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& $HfExe auth whoami *> $null
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$whoamiExit = $LASTEXITCODE
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$ErrorActionPreference = $oldPreference
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if ($whoamiExit -ne 0) {
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Write-Host "Not logged in to Hugging Face. Run one of these first:"
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Write-Host " & `"$HfExe`" auth login"
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Write-Host "or:"
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Write-Host ' $env:HF_TOKEN="YOUR_TOKEN"'
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exit 1
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