antony-bryan-3D2Y
/

phi2-sql-lora-lr2e4

@@ -1,199 +1,92 @@
 ---
-library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
 ## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 ---
+base_model: microsoft/phi-2
+tags:
+  - sql
+  - text-to-sql
+  - lora
+  - qlora
+  - pytorch
+license: mit
+language:
+  - en
 ---
+# Phi-2 SQL LoRA (lr=2e-4)
+Fine-tuned [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on
+[b-mc2/sql-create-context](https://huggingface.co/datasets/b-mc2/sql-create-context)
+using QLoRA — achieving **76% exact match** on SQL generation, up from a 2% baseline.
+This is **Run 1** (lr=2e-4) — the best performing run.
+See also: [phi2-sql-lora-lr5e4](https://huggingface.co/antony-bryan-3D2Y/phi2-sql-lora-lr5e4) (lr=5e-4, 70% EM)
+## Results
+| Model | Exact Match | ROUGE-L | Δ vs Base |
+|---|---|---|---|
+| Phi-2 Base | 2.0% | 0.886 | — |
+| **This model (lr=2e-4)** | **76.0%** | **0.9903** | **+74pp** |
+Evaluated on 50 held-out samples from sql-create-context (seed=42).
+Zero regressions — every query the base model got right, this model also got right.
 ## Training Details
+| Parameter | Value |
+|---|---|
+| Method | QLoRA (4-bit NF4 + LoRA) |
+| LoRA rank | 16 |
+| LoRA alpha | 32 |
+| Target modules | q_proj, v_proj |
+| Dataset | 20,000 samples from sql-create-context |
+| Epochs | 2 |
+| Learning rate | 2e-4 |
+| Effective batch size | 16 |
+| Hardware | Kaggle T4 x2 |
+| Training time | ~7 hours |
+## Usage
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig
+import torch
+model_name = "microsoft/phi-2"
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+tokenizer.pad_token = tokenizer.eos_token
+config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
+config.__dict__['pad_token_id'] = tokenizer.pad_token_id
+base = AutoModelForCausalLM.from_pretrained(
+    model_name, config=config,
+    dtype=torch.float16, device_map="auto", trust_remote_code=True
+)
+model = PeftModel.from_pretrained(base, "antony-bryan-3D2Y/phi2-sql-lora-lr2e4")
+model.eval()
+prompt = """### SQL Schema:
+CREATE TABLE employees (id INT, name VARCHAR, department VARCHAR, salary INT)
+### Question:
+What are the names of employees in the engineering department?
+### SQL Query:
+"""
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+with torch.no_grad():
+    output = model.generate(**inputs, max_new_tokens=100, do_sample=False,
+                            eos_token_id=tokenizer.eos_token_id)
+n = inputs['input_ids'].shape[1]
+result = tokenizer.decode(output[0][n:], skip_special_tokens=True)
+result = result.replace("</s>", "").replace("<|endoftext|>", "").split('\n')[0].strip()
+print(result)
+# → SELECT name FROM employees WHERE department = "engineering"
+```
+## Links
+- 📓 Training notebook: [llm-finetune-eval](https://github.com/antony-bryan/llm-finetune-eval)
+- 📊 W&B training runs: [phi2-sql-finetune](https://wandb.ai/antonybryan2-00-anthropic/phi2-sql-finetune)
+- 🔗 Run 2 (lr=5e-4): [phi2-sql-lora-lr5e4](https://huggingface.co/antony-bryan-3D2Y/phi2-sql-lora-lr5e4)