Text Generation
Transformers
Safetensors
lora
aya
tiny-aya
multilingual
code
legesher
tiny-aya-expedition
language-decoded
unsloth
arxiv:2603.11510
arxiv:2211.15533
arxiv:2510.09591
arxiv:1809.05053
arxiv:2308.16884
arxiv:2106.06937
arxiv:2210.03057
Instructions to use legesher/language-decoded-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use legesher/language-decoded-lora with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="legesher/language-decoded-lora")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("legesher/language-decoded-lora", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use legesher/language-decoded-lora with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "legesher/language-decoded-lora" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "legesher/language-decoded-lora", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/legesher/language-decoded-lora
- SGLang
How to use legesher/language-decoded-lora 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 "legesher/language-decoded-lora" \ --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": "legesher/language-decoded-lora", "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 "legesher/language-decoded-lora" \ --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": "legesher/language-decoded-lora", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Unsloth Desktop
- Docker Model Runner
How to use legesher/language-decoded-lora with Docker Model Runner:
docker model run hf.co/legesher/language-decoded-lora
Commit History
15 days ago
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docs: drop the submitted-paper-title banner (#19)
- docs: drop the submitted-paper-title banner (dc4a9c0711bea0e86861272e7612f42c534ea1a8)ed93e11 -
docs: register the October 2026 seed-123 adapters in the card and manifest (#18)
- docs: register the October 2026 seed-123 adapters in the card and manifest (0d5fd0b518737e909e79b593ecefd134543d656f) - docs: list seed 123 for every Phase 3 condition; drop the batch note (5310edca0436782eebb4f8500d9bbc2e8f4db63b)d0d57de
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Add Attribution & takedown section (#13)
- Add Attribution & takedown section (156dee19a778b3a4a95322c0a84140fe731865a2) - Align body license notes with cc-by-nc-4.0 metadata (c84c57515de9979334640e163218b5dc637653d6)03f675f -
Relicense to cc-by-nc-4.0 (inherits CohereLabs/tiny-aya-base CC-BY-NC-4.0; adapters are adapted material of the NC-licensed base)
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docs: delineate Phase 2 (Stack v1) vs Phase 3 (Stack v2-dedup) adapters (#12)
- docs: delineate Phase 2 (Stack v1) vs Phase 3 (Stack v2-dedup) adapters (5a8663a2db2addd7de162a1ab6c098c723ef3d9c)adeb1c7 -
docs: add arxiv: tags for paper bibliography anchors (#11)
- docs: add arxiv: tags for paper bibliography anchors (b1a44182a68c3e8e7d6e306a70d6edf5f55e86f6)aa01d9a -
docs(readme): align with canonical source-of-truth (cond-5 adapters, source-file control, refined extractor) (#10)
- docs(readme): align with canonical source-of-truth (cond-5 adapters, source-file control, refined extractor) (8306d4c2d2991bb94e55835fc1c5f707596a598f)06802f3 -
docs(readme): cond-5 refined-extractor banner + Phase 3 staleness fixes (#9)
- docs(readme): cond-5 refined-extractor banner + Phase 3 staleness fixes (fa4b0f2e3060814e83f5f27620346f36fd1488bb) - fix(banner): 5 SIB-200 flip cells (was 4) + 4.4× cond-2-ur-5k deflation (was implicit) (e68fb52fba6dff3862185e76f3fc60abb747c135)625893e
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docs: add condition-1-en-32k to Model Structure table (#7)
- docs: add condition-1-en-32k to Model Structure table (d2d4b8e3fbf29ab8a69717c997f43597dc05b3f3)6ac3acd -
docs: fix license to Apache 2.0, add hyperparameters + limitations, update citations (#3)
- docs: fix license to Apache 2.0, add hyperparameters + limitations, update citations (52c78860fd325c1f14657b96be34609c1d835ea4)689d1ea
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Upload model trained with Unsloth
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Update model card: fix conditions, base model, benchmarks, languages (#1)
- Update model card: fix conditions, base model, benchmarks, languages (bf55f60c10d5d40de2f0c7a6653c7e707beef94c)e4f13e7
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init: create README.md
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