Instructions to use tutur90/SONAR-Mini-Text-to-Text with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tutur90/SONAR-Mini-Text-to-Text with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="tutur90/SONAR-Mini-Text-to-Text")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("tutur90/SONAR-Mini-Text-to-Text")
model = AutoModelForSeq2SeqLM.from_pretrained("tutur90/SONAR-Mini-Text-to-Text", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tutur90/SONAR-Mini-Text-to-Text with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tutur90/SONAR-Mini-Text-to-Text"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tutur90/SONAR-Mini-Text-to-Text",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/tutur90/SONAR-Mini-Text-to-Text

SGLang

How to use tutur90/SONAR-Mini-Text-to-Text 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 "tutur90/SONAR-Mini-Text-to-Text" \
    --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": "tutur90/SONAR-Mini-Text-to-Text",
		"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 "tutur90/SONAR-Mini-Text-to-Text" \
        --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": "tutur90/SONAR-Mini-Text-to-Text",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use tutur90/SONAR-Mini-Text-to-Text with Docker Model Runner:
```
docker model run hf.co/tutur90/SONAR-Mini-Text-to-Text
```

tutur90 commited on Sep 30, 2025

Commit

efd9899

verified ·

1 Parent(s): 86de0a5

Upload folder using huggingface_hub

Browse files

Files changed (14) hide show

.gitattributes +1 -0
README.md +53 -0
config.json +35 -0
generation_config.json +9 -0
model.safetensors +3 -0
predict_results.json +616 -0
predict_results_greedy.json +616 -0
sentencepiece.bpe.model +3 -0
special_tokens_map.json +255 -0
tokenizer.json +3 -0
tokenizer_config.json +1880 -0
train_results.json +8 -0
trainer_state.json +0 -0
training_args.bin +3 -0

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 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
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README.md ADDED Viewed

	@@ -0,0 +1,53 @@

+---
+library_name: transformers
+base_model: checkpoints/sonar_mini
+tags:
+- generated_from_trainer
+model-index:
+- name: sonar_mini
+  results: []
+---
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+# sonar_mini
+This model is a fine-tuned version of [checkpoints/sonar_mini](https://huggingface.co/checkpoints/sonar_mini) on an unknown dataset.
+## Model description
+More information needed
+## Intended uses & limitations
+More information needed
+## Training and evaluation data
+More information needed
+## Training procedure
+### Training hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 32
+- eval_batch_size: 512
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 6
+- total_train_batch_size: 192
+- total_eval_batch_size: 3072
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 3.0
+- mixed_precision_training: Native AMP
+### Framework versions
+- Transformers 4.53.2
+- Pytorch 2.7.1+cu126
+- Datasets 4.0.0
+- Tokenizers 0.21.4

config.json ADDED Viewed

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+{
+  "activation_dropout": 0.0,
+  "activation_function": "relu",
+  "architectures": [
+    "SONARForConditionalGeneration"
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+  "init_std": 0.02,
+  "is_encoder_decoder": true,
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+  "max_position_embeddings": 1024,
+  "model_type": "m2m_100",
+  "mse_ratio": 0.2,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "scale_embedding": true,
+  "tokenizer_class": "NllbTokenizer",
+  "torch_dtype": "float16",
+  "transformers_version": "4.53.2",
+  "use_cache": true,
+  "vocab_size": 256206
+}

generation_config.json ADDED Viewed

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+{
+  "_from_model_config": true,
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+  "max_length": 200,
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predict_results_greedy.json ADDED Viewed

	@@ -0,0 +1,616 @@

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+}

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+oid sha256:b3be18cc91c94d4a1d83731ace4dac0b90a7db024edecdeb9fe7d19ec01ce901
+size 32240136

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+}

train_results.json ADDED Viewed

	@@ -0,0 +1,8 @@

+{
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+}

trainer_state.json ADDED Viewed

The diff for this file is too large to render. See raw diff

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