Upload fine-tuned Phi-3 4-bit LoRA adapters

README.md

@@ -1,202 +1,145 @@
----
-base_model: unsloth/phi-3.5-mini-instruct-bnb-4bit
-library_name: peft
----
-
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
+# Model Card for Fine-tuned Phi-3.5-mini-instruct for MCQ Generation
 
 ## Model Details
 
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
+**Model Description**
 
+This model is a fine-tuned version of `unsloth/Phi-3.5-mini-instruct` (an optimized 4-bit version of `microsoft/Phi-3-mini-4k-instruct`). It has been fine-tuned using Low-Rank Adaptation (LoRA) specifically for the task of generating multiple-choice questions (MCQs) in JSON format based on provided context text. The fine-tuning was performed using the script provided in the context.
 
+* **Developed by:** Fine-tuned based on the provided script. Base model by Microsoft. Optimization by Unsloth AI.
+* **Funded by [optional]:** [More Information Needed]
+* **Shared by [optional]:** [More Information Needed]
+* **Model type:** Language Model (Phi-3 architecture) fine-tuned with QLoRA.
+* **Language(s) (NLP):** English
+* **License:** The base model `microsoft/Phi-3-mini-4k-instruct` is licensed under the MIT License. The fine-tuned adapters are subject to the base model's license and potentially the license of the training data (`asanchez75/medical_textbooks_mcq`). Unsloth code is typically Apache 2.0. Please check the specific licenses for compliance.
+* **Finetuned from model:** `unsloth/Phi-3.5-mini-instruct` (4-bit quantized version).
 
-- **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]**
 
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+* **Repository:** [More Information Needed - Link to where the fine-tuned adapters are hosted, if applicable]
+* **Paper [optional]:** [Link to Phi-3 Paper, e.g., https://arxiv.org/abs/2404.14219]
+* **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]
+**Direct Use**
 
-### Downstream Use [optional]
+This model is intended for generating multiple-choice questions (MCQs) in a specific JSON format, given a piece of context text. It requires using the specific prompt structure employed during training (see Preprocessing section). The primary use case involves loading the base `unsloth/Phi-3.5-mini-instruct` model (in 4-bit) and then applying the saved LoRA adapters using the PEFT library.
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+**Downstream Use [optional]**
 
-[More Information Needed]
+Could be integrated into educational tools, content creation pipelines for medical training materials, or automated assessment generation systems within the medical domain.
 
-### Out-of-Scope Use
+**Out-of-Scope Use**
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+* Generating text in formats other than the targeted MCQ JSON structure.
+* Answering general knowledge questions or performing tasks unrelated to MCQ generation from context.
+* Use in domains significantly different from the medical textbook context used for training (performance may degrade).
+* Use without the specific prompt format defined during training.
+* Generating harmful, biased, or inaccurate content.
+* Any use violating the terms of the base model license or the dataset license.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
+* **Inherited Bias:** The model inherits biases present in the base Phi-3 model and the `asanchez75/medical_textbooks_mcq` training dataset, which is derived from medical literature.
+* **Accuracy:** Generated MCQs may be factually incorrect, nonsensical, or poorly formulated. The correctness of the identified "correct\_option" is not guaranteed.
+* **Format Adherence:** While trained to output JSON, the model might occasionally fail to produce perfectly valid JSON or might include extraneous text.
+* **Domain Specificity:** Performance is likely best on medical contexts similar to the training data. Performance on other domains or highly dissimilar medical texts is unknown.
+* **Quantization:** The use of 4-bit quantization (QLoRA) may slightly impact performance compared to a full-precision model, although Unsloth optimizations aim to minimize this.
+* **Context Dependence:** Output quality is highly dependent on the clarity and information content of the provided input context.
+* **Limited Evaluation:** The model was only evaluated qualitatively on one example from the training set within the script. Rigorous evaluation across a dedicated test set was not performed.
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+## Recommendations
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+* **Verification:** Always verify the factual accuracy, grammatical correctness, and appropriateness of generated MCQs before use.
+* **Prompting:** Use the specific prompt structure detailed in the "Preprocessing" section for optimal results.
+* **Testing:** Thoroughly test the model's performance on your specific use case and data distribution.
+* **Bias Awareness:** Be mindful of potential biases inherited from the base model and training data.
+* **JSON Parsing:** Implement robust JSON parsing with error handling for the model's output.
 
 ## 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:**
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-[More Information Needed]
-
-**APA:**
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-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-## More Information [optional]
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-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
-### Framework versions
+Use the code below to load the 4-bit base model, apply the fine-tuned LoRA adapters, and run inference. Replace `"path/to/your/saved/adapters/"` with the actual path where you saved the adapter files (`adapter_model.safetensors`, `adapter_config.json`, etc.) and the tokenizer (`tokenizer.json`, etc.).
+
+```python
+import torch
+from transformers import AutoTokenizer
+from unsloth import FastLanguageModel
+from peft import PeftModel
+import json # For parsing output
+
+# --- Configuration ---
+base_model_name = "unsloth/Phi-3.5-mini-instruct"
+adapter_path = "path/to/your/saved/adapters/" # <--- CHANGE THIS
+max_seq_length = 4096
+
+# --- 1. Load Base Model and Tokenizer (4-bit) ---
+print("Loading base model and tokenizer...")
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = base_model_name,
+    max_seq_length = max_seq_length,
+    dtype = None,
+    load_in_4bit = True, # Load base in 4-bit
+    device_map = "auto",
+)
+print("Base model loaded in 4-bit.")
+
+# Set padding token if necessary
+if tokenizer.pad_token is None:
+    if tokenizer.pad_token_id is None:
+        tokenizer.pad_token = tokenizer.eos_token
+    else:
+        tokenizer.pad_token = tokenizer.convert_ids_to_tokens(tokenizer.pad_token_id)
+tokenizer.padding_side = 'right'
+print(f"Tokenizer pad token: {tokenizer.pad_token}, ID: {tokenizer.pad_token_id}")
+
+# --- 2. Load LoRA Adapters ---
+print(f"Loading LoRA adapters from {adapter_path}...")
+# Load adapters onto the base model
+model = PeftModel.from_pretrained(model, adapter_path)
+print("LoRA adapters loaded.")
+
+# --- 3. Prepare for Inference ---
+print("Preparing combined model for inference...")
+FastLanguageModel.for_inference(model)
+print("Model ready for inference.")
+
+# --- 4. Prepare Inference Prompt ---
+test_context = "Human beings are fallible and it is in their nature to make mistakes. An error of omission occurs when a necessary action has not been taken." # Example context
+inference_prompt = f"<|user|>\nContext:\n{test_context}\n\nGenerate ONE valid multiple-choice question based strictly on the context above. Output ONLY the valid JSON object representing the question.\nMCQ JSON:<|end|>\n<|assistant|>\n"
+
+inputs = tokenizer(inference_prompt, return_tensors="pt", truncation=True, max_length=max_seq_length).to("cuda")
+
+# --- 5. Generate Output ---
+print("Generating MCQ JSON...")
+with torch.no_grad():
+    outputs = model.generate(
+        input_ids = inputs["input_ids"],
+        max_new_tokens=512,        # Max length for the generated JSON
+        temperature=0.1,           # Low temperature for more deterministic output
+        top_p=0.9,
+        do_sample=True,
+        pad_token_id=tokenizer.pad_token_id if tokenizer.pad_token_id is not None else tokenizer.eos_token_id
+    )
+
+# Decode the generated part
+output_ids = outputs[0][inputs["input_ids"].shape[1]:]
+generated_json_part = tokenizer.decode(output_ids, skip_special_tokens=True).strip()
+
+print("\n--- Generated Output ---")
+print(generated_json_part)
+
+# --- 6. (Optional) Validate JSON ---
+try:
+    # Clean up potential markdown fences
+    if generated_json_part.startswith("```json"):
+        generated_json_part = generated_json_part[len("```json"):].strip()
+    if generated_json_part.endswith("```"):
+        generated_json_part = generated_json_part[:-len("```")].strip()
+
+    parsed_json = json.loads(generated_json_part)
+    print("\nGenerated JSON Parsed Successfully:")
+    print(json.dumps(parsed_json, indent=2))
+except json.JSONDecodeError as e:
+    print(f"\nGenerated output IS NOT valid JSON. Error: {e}")
 
-- PEFT 0.15.2