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
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condition-2-ur-20k-seed123
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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 -
rename: cond-3 seed-123 adapter folder to condition-3-zh-5k-native-code-seed123 (#17)
- rename: cond-3 seed-123 adapter folder to condition-3-zh-5k-native-code-seed123 (5aa0713943f7e689a80a9a6a19662cc2727b990a)4e1510f -
phase-2-the-stack-v1-condition-3-zh-5k-seed123 (#16)
- phase-2-the-stack-v1-condition-3-zh-5k-seed123 (f76975be27a185ed554b2ca39528a59bf4c8c631) Co-authored-by: Rafay Mustafa <rafaym@users.noreply.huggingface.co>1cfdcc8 -
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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)
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docs: add arxiv: tags for paper bibliography anchors (#11)
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docs(readme): align with canonical source-of-truth (cond-5 adapters, source-file control, refined extractor) (#10)
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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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- rename: correct cond-3 data-source label — native-code, not the-stack-v1 (8d313365c14162b7cf6e0f4361d6b6924e271f05)78f7bc3 -
rename: drop "phase-2-" prefix on cond-3 adapter, match cond-5 naming
Madison (Pfaff) EdgarUnverified signatureRename tiny-aya-base/phase-2-the-stack-v1-condition-3-zh-5k-seed42 → tiny-aya-base/condition-3-zh-5k-the-stack-v1-seed42 Reorders the path to match the cond-5 convention (condition-N-lang-Nk-<datasource>-seed{N}). The phase-2-ness is no longer encoded in the path — the-stack-v1 data-source suffix is what distinguishes this adapter from any future phase-3 cond-3 trained on a different data source. Pure git rename; LFS objects unchanged. Signed-off-by: Madison (Pfaff) Edgar <7844510+madiedgar@users.noreply.github.com>672dea2
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docs: add condition-1-en-32k to Model Structure table (#7)
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chore: move root adapter to condition-1-en-32k/ subdirectory (#6)
- chore: move root adapter to condition-1-en-32k/ subdirectory (f1dd017c6177852fc48ac41dacfa8ecac028e12c)e4ca1d0