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.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ language:
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+ - en
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+ base_model:
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+ - Qwen/Qwen2.5-Coder-32B-Instruct
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+ new_version: imsanjoykb/sqlCoder-Qwen2.5-8bit
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+ pipeline_tag: text-generation
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+ library_name: adapter-transformers
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+ tags:
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+ - unsloth,
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+ - pytorch,
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+ - inference-endpoint,
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+ - sql-code-generation,
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+ ---
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+ [🤗 HF Repo](https://huggingface.co/imsanjoykb/sqlCoder-Qwen2.5-8bit) | [♾️ Colab](https://colab.research.google.com/drive/19e-u32GY2y5lsckNuWhBQExvXgVn8ZjG?usp=sharing)
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+
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+ Introducing the latest fine-tuned version of Qwen2.5-Coder-14B-Instruct, specifically tailored for SQL code generation. Built on the robust 14-billion parameter Qwen2.5-Coder architecture, this model leverages advanced configurations like bfloat16 precision and a custom quantization setup, optimized for efficient 4-bit computation. With a maximum context window of 32K tokens, this model supports extensive SQL sequences and complex query generation without compromising accuracy or performance.
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+
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+ Our fine-tuning process has enriched this model with domain-specific SQL patterns and nuanced query constructions, making it exceptionally adept at handling real-world SQL requirements, from query creation to debugging and optimization. By combining Qwen2.5's foundational strengths with targeted training on custom SQL data, this model achieves a powerful balance of general-purpose code understanding and SQL-specific precision, making it an ideal tool for developers and data engineers seeking top-tier SQL generation capabilities.
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+
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+
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+ ## Inference
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+
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+ Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
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+
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+ ```python
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+ # Import necessary libraries
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+ from unsloth import FastLanguageModel
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+ import torch
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+
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+ # Define the model name and other parameters
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+ model_name = "imsanjoykb/sqlCoder-Qwen2.5-8bit"
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+ max_seq_length = 2048
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+ dtype = None
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+ load_in_4bit = True
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+
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+ # Load the model and tokenizer from Hugging Face
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+ model, tokenizer = FastLanguageModel.from_pretrained(
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+ model_name=model_name,
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+ max_seq_length=max_seq_length,
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+ dtype=dtype,
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+ load_in_4bit=load_in_4bit,
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+ )
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+
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+ # Enable faster inference
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+ FastLanguageModel.for_inference(model)
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+
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+ # Define the prompt template
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+ odoo_text2sql_prompt = """Below is an instruction describing a task related to generating a SQL query specifically for Odoo's database structure. The input provides relevant context about Odoo models or data fields from {db_schema}. Write a SQL query that fulfills the given task using Odoo's database schema.
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+
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+ ### Instruction:
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+ Generate a SQL query in the context of Odoo to {}
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+
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+ ### Input:
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+ {}
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+
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+ ### Response:
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+ {}
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+ """
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+
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+ # Optionally, use a TextStreamer for continuous inference
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+ from transformers import TextStreamer
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+
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+ # Prepare the input text for continuous inference
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+ instruction = ""
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+ input_text = "What is the top profitable product?"
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+ output_text = ""
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+
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+ # Tokenize the input text
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+ inputs = tokenizer(
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+ [
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+ odoo_text2sql_prompt.format(instruction, input_text, output_text)
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+ ],
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+ return_tensors="pt"
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+ ).to("cuda")
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+
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+ # Initialize the TextStreamer
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+ text_streamer = TextStreamer(tokenizer)
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+
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+ # Generate the output using the model with TextStreamer
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+ _ = model.generate(**inputs, streamer=text_streamer, max_new_tokens=350)
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+ ```
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+ ## Model Download
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+ | **Model** | **#Total Params** | **#Active Params** | **Context Length** | **Download** |
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+ | :-----------------------------: | :---------------: | :----------------: | :----------------: | :----------------------------------------------------------: |
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+ | sqlCoder-Qwen2.5-8bit | 14B | 2.4B | 128k | [🤗 HuggingFace](https://huggingface.co/imsanjoykb/sqlCoder-Qwen2.5-8bit) |
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+
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+ # Uploaded model
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+
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+ - **Developed by:** [Sanjoy Biswas](https://www.linkedin.com/in/imsanjoykb/)
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+ - **License:** apache-2.0
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