updated

Browse files

Files changed (1) hide show

README.md +50 -193

README.md CHANGED Viewed

@@ -1,209 +1,66 @@
 ---
-base_model: deepseek-ai/deepseek-coder-1.3b-instruct
-library_name: peft
 pipeline_tag: text-generation
 tags:
-- base_model:adapter:deepseek-ai/deepseek-coder-1.3b-instruct
 - lora
-- sft
-- transformers
 - trl
 ---
-# 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. -->
-- **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]
-### Framework versions
-- PEFT 0.18.0

 ---
+language: en
 pipeline_tag: text-generation
 tags:
+- deepseek
+- deepseek-coder
+- peft
 - lora
+- qlora
 - trl
+- sft
+- mcq
+- education
+- docker
+- fastapi
+- python
+- rag
+license: other
+base_model: deepseek-ai/deepseek-coder-1.3b-instruct
 ---
+# DeepSeek MCQ Trainer (LoRA) — by Mohammed Sayeeduddin
 ## Model Details
+- **Model name:** `psdba/deepseek-mcq-trainer-mohammedsayeeduddin-lora`
+- **Developed by:** **Mohammed Sayeeduddin**
+- **Model type:** LoRA adapter (PEFT) fine-tuned for structured MCQ generation
+- **Base model:** `deepseek-ai/deepseek-coder-1.3b-instruct`
+- **Primary use:** Instructor-grade MCQ generation in **strict JSON** format for IT training
+- **Language(s):** English
+- **License:** This repository contains **LoRA adapter weights only**. Usage is subject to the license of the **base model** (`deepseek-ai/deepseek-coder-1.3b-instruct`). Please review and comply with that license before commercial or redistribution use.
+## Model Description
+This is a **specialist training model** designed to generate multiple-choice questions (MCQs) in a strict, machine-readable JSON schema.
+The adapter was fine-tuned to produce:
+- 4 options (A/B/C/D)
+- exactly 1 correct answer
+- short explanation
+- JSON-only responses (no markdown, no extra commentary)
+## Intended Use
 ### Direct Use
+- Corporate training MCQs (FastAPI, Docker/Linux, Python Core, LLM/RAG)
+- Classroom quizzes and practice tests
+- Building MCQ datasets for LMS/Excel ingestion
+### Downstream Use
+- Integration into training platforms (MCQ generators, exam portals)
+- Dataset generation pipelines (JSON → CSV → LMS)
+## Out-of-Scope Use
+- Medical / legal / financial advice
+- Open-domain chat or creative writing
+- High-stakes decisions without human validation
 ## Bias, Risks, and Limitations
+- MCQs may still contain imperfections or ambiguous distractors.
+- Always validate questions before real exams or certifications.
+- Model can hallucinate if prompts are unclear or request unsupported topics.
+## How to Get Started
+### Install
+```bash
+pip install -U transformers peft accelerate