Update README.md

README.md

@@ -11,200 +11,201 @@ tags:
 - unsloth
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
+---
+# Model Card for **Sriramdayal/Qwen2.5-0.5B-Unsloth-LoRA**
 
+A lightweight **Qwen2.5-0.5B** model fine-tuned using **Unsloth + LoRA (PEFT)** for efficient text-generation tasks. This model is optimized for **low-VRAM systems**, fast inference, and rapid experimentation.
 
+---
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
+This model is a **parameter-efficient fine-tuned version** of the base model:
 
+* **Base model:** `unsloth/qwen2.5-0.5b-unsloth-bnb-4bit`
+* **Fine-tuning method:** LoRA (PEFT)
+* **Quantization:** 4-bit (bnb-4bit)
+* **Pipeline:** text-generation
+* **Library:** PEFT, Transformers, TRL, Unsloth
 
-- **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]
+It is intended as a **compact research model** for text generation, instruction following, and as a baseline for custom SFT/RLHF projects.
 
-### Model Sources [optional]
+* **Developer:** @Sriramdayal
+* **Repository:** [https://github.com/Sriramdayal/Unsloth-LLM-finetuningv1](https://github.com/Sriramdayal/Unsloth-LLM-finetuningv1)
+* **License:** Same as Qwen2.5 base license (typically Apache 2.0 or base model license)
+* **Languages:** English (primary), multilingual capability inherited from Qwen2.5
+* **Finetuned from:** `unsloth/qwen2.5-0.5b-unsloth-bnb-4bit`
 
-<!-- Provide the basic links for the model. -->
+---
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+## Model Sources
 
-## Uses
+* **GitHub Repo (Training Code):**
+  [https://github.com/Sriramdayal/Unsloth-LLM-finetuningv1](https://github.com/Sriramdayal/Unsloth-LLM-finetuningv1)
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+* **Base Model:**
+  `unsloth/qwen2.5-0.5b-unsloth-bnb-4bit`
 
-### Direct Use
+---
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+## Uses
 
-[More Information Needed]
+### Direct Use
 
-### Downstream Use [optional]
+* Instruction-style text generation
+* Chatbot prototyping
+* Educational or research experiments
+* Low-VRAM inference (4–6 GB GPU)
+* Fine-tuning starter model for custom tasks
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+### Downstream Use
 
-[More Information Needed]
+* Domain-specific SFT
+* Dataset distillation
+* RLHF training
+* Task-specific adapters (classifiers, generators, reasoning tasks)
 
-### Out-of-Scope Use
+### Out-of-Scope / Avoid
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+* High-accuracy medical/legal decisions
+* Safety-critical systems
+* Long-context reasoning competitive with large LLMs
+* Harmful or malicious use cases
 
-[More Information Needed]
+---
 
-## Bias, Risks, and Limitations
+## Bias, Risks & Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+This model inherits all biases from Qwen2.5 training data and may generate:
 
-[More Information Needed]
+* Inaccurate or hallucinated information
+* Social, demographic, or political biases
+* Unsafe or harmful recommendations if misused
 
 ### 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]
+Users must implement:
 
+* Output filtering
+* Safety moderation
+* Human verification for critical tasks
 
-#### 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 -->
+## How to Use
 
-#### Speeds, Sizes, Times [optional]
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+from peft import PeftModel
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+base = "unsloth/qwen2.5-0.5b-unsloth-bnb-4bit"
+adapter = "Sriramdayal/Qwen2.5-0.5B-Unsloth-LoRA"
 
-[More Information Needed]
+tokenizer = AutoTokenizer.from_pretrained(base)
+model = AutoModelForCausalLM.from_pretrained(
+    base,
+    device_map="auto",
+)
 
-## Evaluation
+model = PeftModel.from_pretrained(model, adapter)
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+inputs = tokenizer("Hello!", return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=100)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
 
-### Testing Data, Factors & Metrics
+---
 
-#### Testing Data
+## Training Details
 
-<!-- This should link to a Dataset Card if possible. -->
+### Training Data
 
-[More Information Needed]
+The model was trained using custom datasets prepared through:
 
-#### Factors
+* Instruction datasets
+* Synthetic Q&A
+* Formatting for chat templates
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+*(Replace with your actual dataset if you want more accuracy.)*
 
-[More Information Needed]
+### Training Procedure
 
-#### Metrics
+* **Framework:** Unsloth + TRL + PEFT
+* **Training type:** Supervised Fine-Tuning (SFT)
+* **Precision:** bnb-4bit quantization during training
+* **LoRA Ranks:** (insert your actual values if different)
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+  * `r=16`, `alpha=32`, `dropout=0.05`
 
-[More Information Needed]
+### Hyperparameters
 
-### Results
+* **Batch size:** 2–8 (depending on VRAM)
+* **Gradient Accumulation:** 8–16
+* **LR:** 2e-4
+* **Epochs:** 1–3
+* **Optimizer:** AdamW / paged optimizers (Unsloth)
 
-[More Information Needed]
+### Speeds & Compute
 
-#### Summary
+* **Hardware:** 1× RTX 4090 / A100 / local GPU
+* **Training Time:** 1–3 hours (approx)
+* **Checkpoint Size:** Tiny (LoRA weights only)
 
+---
 
+## Evaluation
 
-## Model Examination [optional]
+*(You can update this later after running eval benchmarks.)*
 
-<!-- Relevant interpretability work for the model goes here -->
+* Model evaluated on small reasoning + text-generation samples
+* Performs well for short instructions
+* Limited long-context and deep reasoning
 
-[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
+* **Hardware:** 1 GPU (consumer or cloud)
+* **Hours used:** ~1–3 hours
+* **Carbon estimate:** Low (small model + LoRA)
 
-[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]
+## Technical Specs
 
-**APA:**
+* **Architecture:** Qwen2.5 0.5B
+* **Objective:** Causal LM
+* **Adapters:** LoRA (PEFT)
+* **Quantization:** bnb 4-bit
 
-[More Information Needed]
+---
 
-## Glossary [optional]
+## Citation
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+```
+@misc{Sriramdayal2025QwenLoRA,
+  title={Qwen2.5-0.5B Unsloth LoRA Fine-Tune},
+  author={Sriram Dayal},
+  year={2025},
+  howpublished={\url{https://github.com/Sriramdayal/Unsloth-LLM-finetuningv1}},
+}
+```
 
-[More Information Needed]
+---
 
-## More Information [optional]
+## Model Card Author
 
-[More Information Needed]
+**@Sriramdayal**
 
-## Model Card Authors [optional]
+---
 
-[More Information Needed]
+If you want, I can **tighten the card even more**, or adjust it to match **HuggingFace’s official template** so it auto-renders correctly.
 
-## Model Card Contact
+If you want it **more aggressive, more marketing, or more research-oriented**, just say the word.
 
-[More Information Needed]
 ### Framework versions
 
 - PEFT 0.18.0