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
Upload folder using huggingface_hub
Browse files
tiny-aya-base/condition-1-en-5k-seed456/adapter_config.json
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{
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"alora_invocation_tokens": null,
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"alpha_pattern": {},
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"arrow_config": null,
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"auto_mapping": {
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"base_model_class": "Cohere2ForCausalLM",
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"parent_library": "transformers.models.cohere2.modeling_cohere2",
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"unsloth_fixed": true
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},
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"base_model_name_or_path": "CohereLabs/tiny-aya-base",
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"bias": "none",
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"corda_config": null,
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"ensure_weight_tying": false,
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"eva_config": null,
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"exclude_modules": null,
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"fan_in_fan_out": false,
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"inference_mode": true,
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"init_lora_weights": true,
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"layer_replication": null,
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"layers_pattern": null,
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"layers_to_transform": null,
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"loftq_config": {},
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"lora_alpha": 32,
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"lora_bias": false,
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"lora_dropout": 0.0,
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"megatron_config": null,
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"megatron_core": "megatron.core",
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"modules_to_save": null,
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"peft_type": "LORA",
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"peft_version": "0.18.1",
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"qalora_group_size": 16,
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"r": 16,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"k_proj",
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"gate_proj",
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"q_proj",
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"o_proj",
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"down_proj",
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"v_proj",
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"up_proj"
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],
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"target_parameters": null,
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"task_type": "CAUSAL_LM",
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"trainable_token_indices": null,
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"use_dora": false,
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"use_qalora": false,
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"use_rslora": false
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}
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tiny-aya-base/condition-1-en-5k-seed456/adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:9eb5310f40499ba04d3ece15b802773b89b671e412221b04b106f55d8ac161a7
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size 120981200
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tiny-aya-base/condition-1-en-5k-seed456/training_metrics.json
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{
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"model_config_name": "tiny-aya-base",
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"condition_name": "condition-1-en-5k",
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"seed": 456,
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"output_name": "condition-1-en-5k-seed456",
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"train_result": {
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"train_runtime": 378.9794,
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"total_flos": 8941999906357248.0,
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"train_loss": 1.1095178946852684,
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"epoch": 1.0
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},
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"log_history": [
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{
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"loss": 1.0991,
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"grad_norm": 0.06809136271476746,
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"learning_rate": 0.00017431448254773944,
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"epoch": 0.3125,
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"step": 10
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},
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{
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"loss": 1.1506,
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"grad_norm": 0.07006137073040009,
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"learning_rate": 7.920883091822408e-05,
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"epoch": 0.625,
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"step": 20
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},
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{
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"loss": 1.1078,
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"grad_norm": 0.06964733451604843,
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"learning_rate": 4.8943483704846475e-06,
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"epoch": 0.9375,
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"step": 30
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},
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{
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"train_runtime": 378.9794,
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"train_samples_per_second": 1.319,
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"train_steps_per_second": 0.084,
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"step": 32
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]
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}
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