Text Generation
Transformers
TensorBoard
Safetensors
English
alicet5_moe
text2text-generation
pretrained
from-scratch
tiny-llm-ablation
custom_code
ul2
Mixture of Experts
encoder-decoder
Eval Results (legacy)
Instructions to use d0rj/t5-moe-55M-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use d0rj/t5-moe-55M-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="d0rj/t5-moe-55M-base", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForSeq2SeqLM model = AutoModelForSeq2SeqLM.from_pretrained("d0rj/t5-moe-55M-base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use d0rj/t5-moe-55M-base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "d0rj/t5-moe-55M-base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "d0rj/t5-moe-55M-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/d0rj/t5-moe-55M-base
- SGLang
How to use d0rj/t5-moe-55M-base 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 "d0rj/t5-moe-55M-base" \ --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": "d0rj/t5-moe-55M-base", "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 "d0rj/t5-moe-55M-base" \ --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": "d0rj/t5-moe-55M-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use d0rj/t5-moe-55M-base with Docker Model Runner:
docker model run hf.co/d0rj/t5-moe-55M-base
Download evaluation/results.json from d0rj/t5-moe-55M-base: direct link, hf CLI and curl.
- Browser
- Download file 27.9 kB
-
https://huggingface.co/d0rj/t5-moe-55M-base/resolve/main/evaluation/results.json
- Command line
-
hf download hf://d0rj/t5-moe-55M-base/evaluation/results.json
-
curl -L -o results.json https://huggingface.co/d0rj/t5-moe-55M-base/resolve/main/evaluation/results.json
27.9 kB
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| "truthfulqa_mc2": { | |
| "label": "TruthfulQA MC2", | |
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| "bananamind_base": { | |
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| "mmlu_continuation": { | |
| "label": "MMLU continuation", | |
| "dataset": "cais/mmlu", | |
| "config": "57 subjects", | |
| "split": "test", | |
| "samples": 14042, | |
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| "result_path": "runs/eval/cria-backfill-20261001/t5/attempt-001/mmlu_continuation.json", | |
| "result_sha256": "8e939afec45700c2d5f70dafa5df87b52841ace350b74969bbfcacc9fc1e54f8", | |
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