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Model: defog/sqlcoder-34b-alpha
Source: Original Platform

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README.md

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+---
+license: cc-by-4.0
+language:
+- en
+pipeline_tag: text-generation
+---
+
+# Defog SQLCoder
+**Updated on Nov 14 to reflect benchmarks for SQLCoder-34B**
+
+Defog's SQLCoder is a state-of-the-art LLM for converting natural language questions to SQL queries.
+
+[Interactive Demo](https://defog.ai/sqlcoder-demo/) | [🤗 HF Repo](https://huggingface.co/defog/sqlcoder-34b-alpha) | [♾️ Colab](https://colab.research.google.com/drive/1z4rmOEiFkxkMiecAWeTUlPl0OmKgfEu7?usp=sharing) | [🐦 Twitter](https://twitter.com/defogdata)
+
+## TL;DR
+SQLCoder-34B is a 34B parameter model that outperforms `gpt-4` and `gpt-4-turbo` for natural language to SQL generation tasks on our [sql-eval](https://github.com/defog-ai/sql-eval) framework, and significantly outperforms all popular open-source models.
+
+SQLCoder-34B is fine-tuned on a base CodeLlama model.
+
+## Results on novel datasets not seen in training
+| model   | perc_correct |
+|-|-|
+| defog-sqlcoder-34b    | 84.0 |
+| gpt4-turbo-2023-11-09 | 82.5 |
+| gpt4-2023-11-09       | 82.5 |
+| defog-sqlcoder2       | 77.5 |
+| gpt4-2023-08-28       | 74.0 |
+| defog-sqlcoder-7b     | 71.0 |
+| gpt-3.5-2023-10-04    | 66.0 |
+| claude-2              | 64.5 |
+| gpt-3.5-2023-08-28    | 61.0 |
+| claude_instant_1      | 61.0 |
+| text-davinci-003      | 52.5 |
+
+![image](https://github.com/defog-ai/sqlcoder/assets/5008293/caed3423-8e86-4952-9da1-1a5e016a4696)
+
+
+## License
+The code in this repo (what little there is of it) is Apache-2 licensed. The model weights have a `CC BY-SA 4.0` license. The TL;DR is that you can use and modify the model for any purpose – including commercial use. However, if you modify the weights (for example, by fine-tuning), you must open-source your modified weights under the same license terms.
+
+## Training
+Defog was trained on more than 20,000 human-curated questions. These questions were based on 10 different schemas. None of the schemas in the training data were included in our evaluation framework. 
+
+You can read more about our [training approach](https://defog.ai/blog/open-sourcing-sqlcoder2-7b/) and [evaluation framework](https://defog.ai/blog/open-sourcing-sqleval/).
+
+## Results by question category
+We classified each generated question into one of 5 categories. The table displays the percentage of questions answered correctly by each model, broken down by category.
+|                | date | group_by | order_by | ratio | join | where |
+| -------------- | ---- | -------- | -------- | ----- | ---- | ----- |
+| sqlcoder-34b   | 80   | 94.3     | 88.6     | 74.3  | 82.9 | 82.9  |
+| gpt-4          | 68   | 94.3     | 85.7     | 77.1  | 85.7 | 80    |
+| sqlcoder2-15b  | 76   | 80       | 77.1     | 60    | 77.1 | 77.1  |
+| sqlcoder-7b    | 64   | 82.9     | 74.3     | 54.3  | 74.3 | 74.3  |
+| gpt-3.5        | 68   | 77.1     | 68.6     | 37.1  | 71.4 | 74.3  |
+| claude-2       | 52   | 71.4     | 74.3     | 57.1  | 65.7 | 62.9  |
+| claude-instant | 48   | 71.4     | 74.3     | 45.7  | 62.9 | 60    |
+| gpt-3          | 32   | 71.4     | 68.6     | 25.7  | 57.1 | 54.3  |
+
+<img width="831" alt="image" src="https://github.com/defog-ai/sqlcoder/assets/5008293/79c5bdc8-373c-4abd-822e-e2c2569ed353">
+
+
+## Using SQLCoder
+You can use SQLCoder via the `transformers` library by downloading our model weights from the Hugging Face repo. We have added sample code for [inference](./inference.py) on a [sample database schema](./metadata.sql). 
+```bash
+python inference.py -q "Question about the sample database goes here"
+
+# Sample question:
+# Do we get more revenue from customers in New York compared to customers in San Francisco? Give me the total revenue for each city, and the difference between the two.
+```
+
+You can also use a demo on our website [here](https://defog.ai/sqlcoder-demo)
+
+## Hardware Requirements
+SQLCoder-34B has been tested on a 4xA10 GPU with `float16` weights. You can also load an 8-bit and 4-bit quantized version of the model on consumer GPUs with 20GB or more of memory – like RTX 4090, RTX 3090, and Apple M2 Pro, M2 Max, or M2 Ultra Chips with 20GB or more of memory.
+
+## Todo
+
+- [x] Open-source the v1 model weights
+- [x] Train the model on more data, with higher data variance
+- [ ] Tune the model further with Reward Modelling and RLHF
+- [ ] Pretrain a model from scratch that specializes in SQL analysis

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+{
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+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 8192,
+  "initializer_range": 0.02,
+  "intermediate_size": 22016,
+  "max_position_embeddings": 16384,
+  "model_type": "llama",
+  "num_attention_heads": 64,
+  "num_hidden_layers": 48,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.34.1",
+  "use_cache": true,
+  "vocab_size": 32000
+}

configuration.json

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generation_config.json

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+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.34.1"
+}

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+    "▁<EOT>",
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+    "▁<EOT>"
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+      "content": "▁<EOT>",
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+    "▁<EOT>",
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+    "▁<EOT>"
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+  "eot_token": "▁<EOT>",
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