Upload T5-LM-Large-text2sql-spider ONNX model

README.md

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+---
+library_name: onnx
+tags:
+  - text2text-generation
+  - t5
+  - text-to-sql
+  - sql
+  - spider
+  - encoder-decoder
+  - onnx
+  - inference4j
+license: apache-2.0
+pipeline_tag: text2text-generation
+datasets:
+  - spider
+  - spider-syn
+---
+
+# T5-LM-Large text2sql-spider — ONNX
+
+ONNX export of [T5-LM-Large-text2sql-spider](https://huggingface.co/gaussalgo/T5-LM-Large-text2sql-spider) (0.8B parameters) with encoder-decoder architecture and KV cache support.
+
+This is a T5-large model fine-tuned on the Spider and Spider-Syn datasets for text-to-SQL generation. Given a natural language question and a database schema, it produces the corresponding SQL query.
+
+Converted for use with [inference4j](https://github.com/inference4j/inference4j), an inference-only AI library for Java.
+
+## Original Source
+
+- **Repository:** [gaussalgo/T5-LM-Large-text2sql-spider](https://huggingface.co/gaussalgo/T5-LM-Large-text2sql-spider)
+- **Base model:** [google/t5-large-lm-adapt](https://huggingface.co/google/t5-large-lm-adapt)
+- **License:** Apache 2.0
+
+## Usage with inference4j
+
+```java
+try (var sqlGen = T5SqlGenerator.t5LargeSpider().build()) {
+    String sql = sqlGen.generateSql(
+        "How many employees are in each department?",
+        "\"employees\" \"id\" int, \"name\" varchar, \"dept_id\" int "
+        + "[SEP] \"departments\" \"id\" int, \"name\" varchar");
+    System.out.println(sql);
+}
+```
+
+## Schema Format
+
+The model expects the schema in the following format:
+
+```
+"table_name" "col1" type, "col2" type, foreign_key: "table"."col" = "other"."col" primary key: "col" [SEP] "table2" ...
+```
+
+- Table and column names are double-quoted
+- Columns are comma-separated with types
+- Tables are separated by `[SEP]`
+- Foreign keys and primary keys are declared per table
+
+## Model Details
+
+| Property | Value |
+|----------|-------|
+| Architecture | T5 encoder-decoder (0.8B parameters) |
+| Task | Text-to-SQL generation |
+| Training data | Spider, Spider-Syn |
+| Tokenizer | SentencePiece (32,128 tokens) |
+| Original framework | PyTorch (transformers) |
+| Export method | Hugging Face Optimum (encoder-decoder with KV cache) |
+
+## License
+
+This model is licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0). Original model by [Gaussalgo](https://huggingface.co/gaussalgo), base model by [Google](https://huggingface.co/google).

chat_template.jinja

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+{% if not add_generation_prompt is defined %}
+{% set add_generation_prompt = false %}
+{% endif %}
+{%- set ns = namespace(found=false) -%}
+{%- for message in messages -%}
+    {%- if message['role'] == 'system' -%}
+        {%- set ns.found = true -%}
+    {%- endif -%}
+{%- endfor -%}
+{{bos_token}}{%- if not ns.found -%}
+{{'You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer\n'}}
+{%- endif %}
+{%- for message in messages %}
+    {%- if message['role'] == 'system' %}
+{{ message['content'] }}
+    {%- else %}
+        {%- if message['role'] == 'user' %}
+{{'### Instruction:\n' + message['content'] + '\n'}}
+        {%- else %}
+{{'### Response:\n' + message['content'] + '\n<|EOT|>\n'}}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{% if add_generation_prompt %}
+{{'### Response:'}}
+{% endif %}

config.json

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+{
+  "architectures": [
+    "T5ForConditionalGeneration"
+  ],
+  "classifier_dropout": 0.0,
+  "d_ff": 2816,
+  "d_kv": 64,
+  "d_model": 1024,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "gelu_new",
+  "dropout_rate": 0.1,
+  "dtype": "float32",
+  "eos_token_id": 1,
+  "feed_forward_proj": "gated-gelu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "num_decoder_layers": 24,
+  "num_heads": 16,
+  "num_layers": 24,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.6",
+  "use_cache": true,
+  "vocab_size": 32128
+}

decoder_model.onnx

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+oid sha256:352f7316dd381b1e326577aea0183f7e09852340f04c499b867c3c4a3896b398
+size 1900001851

decoder_with_past_model.onnx

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+oid sha256:99afdd41369f574fc64b4d16d0ebdb291f44c64f2cfce71d0b8f0cb4c270c669
+size 1698604355

encoder_model.onnx

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+size 1365296186