Instructions to use vocabtrimmer/mbart-large-cc25-trimmed-ru-ruquad-qa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vocabtrimmer/mbart-large-cc25-trimmed-ru-ruquad-qa with Transformers:

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

pipe = pipeline("text-generation", model="vocabtrimmer/mbart-large-cc25-trimmed-ru-ruquad-qa")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("vocabtrimmer/mbart-large-cc25-trimmed-ru-ruquad-qa")
model = AutoModelForSeq2SeqLM.from_pretrained("vocabtrimmer/mbart-large-cc25-trimmed-ru-ruquad-qa")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use vocabtrimmer/mbart-large-cc25-trimmed-ru-ruquad-qa with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vocabtrimmer/mbart-large-cc25-trimmed-ru-ruquad-qa"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vocabtrimmer/mbart-large-cc25-trimmed-ru-ruquad-qa",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/vocabtrimmer/mbart-large-cc25-trimmed-ru-ruquad-qa

SGLang

How to use vocabtrimmer/mbart-large-cc25-trimmed-ru-ruquad-qa 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 "vocabtrimmer/mbart-large-cc25-trimmed-ru-ruquad-qa" \
    --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": "vocabtrimmer/mbart-large-cc25-trimmed-ru-ruquad-qa",
		"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 "vocabtrimmer/mbart-large-cc25-trimmed-ru-ruquad-qa" \
        --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": "vocabtrimmer/mbart-large-cc25-trimmed-ru-ruquad-qa",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use vocabtrimmer/mbart-large-cc25-trimmed-ru-ruquad-qa with Docker Model Runner:
```
docker model run hf.co/vocabtrimmer/mbart-large-cc25-trimmed-ru-ruquad-qa
```

asahi417 commited on Apr 8, 2023

Commit

0245e32

1 Parent(s): 9c21e1e

Upload MBartForConditionalGeneration

Browse files

Files changed (3) hide show

config.json +81 -0
generation_config.json +10 -0
pytorch_model.bin +3 -0

config.json ADDED Viewed

	@@ -0,0 +1,81 @@

+{
+  "_name_or_path": "lmqg_output/trimmed_qa/mbart-large-cc25-trimmed-ru-ruquad-qa/best_model",
+  "_num_labels": 3,
+  "activation_dropout": 0.0,
+  "activation_function": "gelu",
+  "add_bias_logits": false,
+  "add_final_layer_norm": true,
+  "add_prefix": false,
+  "architectures": [
+    "MBartForConditionalGeneration"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "classif_dropout": 0.0,
+  "classifier_dropout": 0.0,
+  "d_model": 1024,
+  "decoder_attention_heads": 16,
+  "decoder_ffn_dim": 4096,
+  "decoder_layerdrop": 0.0,
+  "decoder_layers": 12,
+  "dropout": 0.1,
+  "encoder_attention_heads": 16,
+  "encoder_ffn_dim": 4096,
+  "encoder_layerdrop": 0.0,
+  "encoder_layers": 12,
+  "eos_token_id": 2,
+  "forced_eos_token_id": 2,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2"
+  },
+  "init_std": 0.02,
+  "is_encoder_decoder": true,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2
+  },
+  "max_length": 1024,
+  "max_position_embeddings": 1024,
+  "model_type": "mbart",
+  "normalize_before": true,
+  "normalize_embedding": true,
+  "num_beams": 5,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 1,
+  "scale_embedding": true,
+  "static_position_embeddings": false,
+  "task_specific_params": {
+    "translation_en_to_ro": {
+      "decoder_start_token_id": 250020
+    }
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.26.1",
+  "use_cache": true,
+  "vocab_size": 99662,
+  "vocabtrimmer": {
+    "mining_config": {
+      "dataset": "vocabtrimmer/mc4_validation",
+      "dataset_column": "text",
+      "dataset_name": "ru",
+      "dataset_split": "validation",
+      "language": "ru",
+      "min_frequency": 2,
+      "target_vocab_size": null
+    },
+    "stats": {
+      "compression_rate_embedding": 39.86009510972815,
+      "compression_rate_full": 74.79343207020544,
+      "parameter_size_embedding/raw": 512055296,
+      "parameter_size_embedding/trimmed": 204105728,
+      "parameter_size_full/raw": 610851840,
+      "parameter_size_full/trimmed": 456877056,
+      "vocab_size/raw": 250027,
+      "vocab_size/trimmed": 99661
+    }
+  }
+}

generation_config.json ADDED Viewed

	@@ -0,0 +1,10 @@

+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "forced_eos_token_id": 2,
+  "max_length": 1024,
+  "num_beams": 5,
+  "pad_token_id": 1,
+  "transformers_version": "4.26.1"
+}

pytorch_model.bin ADDED Viewed

	@@ -0,0 +1,3 @@

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