Update README.md

README.md

@@ -1,199 +1,139 @@
 ---
-library_name: transformers
-tags: []
+license: apache-2.0
+base_model: meta-llama/Llama-3.2-1B-Instruct
+tags:
+- dpo
+- lora
+- peft
+- llama-3.2
+- iterative-dpo
+- self-rewarding
+library_name: peft
 ---
 
-# Model Card for Model ID
+# Iterative DPO Fine-Tune of Llama-3.2-1B (Iteration 2)
 
-<!-- Provide a quick summary of what the model is/does. -->
+This repository contains the LoRA adapters from the **second and final iteration** of a Direct Preference Optimization (DPO) fine-tuning process on the `meta-llama/Llama-3.2-1B-Instruct` model.
 
+This model represents a further refinement of the Iteration 1 model, demonstrating a self-improvement loop where the model learns from preferences on its own generated outputs. This work was inspired by the "Self-Rewarding Language Models" paper.
 
+- **Repository for Iteration 1:** [NilayR/llama32-iterative-dpo-iter1](https://huggingface.co/NilayR/llama32-iterative-dpo-iter1)
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+This model is the result of the second fine-tuning cycle in an iterative DPO pipeline. The process began with the model from Iteration 1 generating a new set of responses. These responses were then evaluated by an LLM Judge (GPT-3.5-Turbo) to create a fresh preference dataset. This new dataset was used to further fine-tune the model, resulting in the adapters contained in this repository.
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+The goal of this iteration was to demonstrate that the model could continue to improve its alignment with desired behaviors (accuracy, helpfulness, clarity) using its own outputs as a foundation for learning.
 
-- **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.
+- **Developed by:** NilayR
+- **Model type:** Causal Language Model
+- **Language(s):** English
+- **License:** apache-2.0
+- **Finetuned from model:** `meta-llama/Llama-3.2-1B-Instruct` (with adapters from Iteration 1)
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
+To use these LoRA adapters, load the base model (`meta-llama/Llama-3.2-1B-Instruct`) and then apply the adapters from this repository.
+
+```python
+import torch
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+
+# Set base model ID and adapter path
+base_model_id = "meta-llama/Llama-3.2-1B-Instruct"
+adapter_id = "NilayR/llama32-iterative-dpo-iter2"
+
+# Configure BitsAndBytes for 4-bit quantization
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16
+)
+
+# Load the base model with quantization
+base_model = AutoModelForCausalLM.from_pretrained(
+    base_model_id,
+    quantization_config=bnb_config,
+    device_map="auto",
+    trust_remote_code=True,
+)
+
+# Load the tokenizer
+tokenizer = AutoTokenizer.from_pretrained(base_model_id)
+tokenizer.pad_token = tokenizer.eos_token
+
+# Load and apply the PEFT adapters
+model = PeftModel.from_pretrained(base_model, adapter_id)
+
+# --- Generate a response ---
+prompt = "What are the key benefits of meditation?"
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": prompt}
+]
+
+input_ids = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    return_tensors="pt"
+).to(model.device)
+
+outputs = model.generate(
+    input_ids,
+    max_new_tokens=200,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.95
+)
+
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response.split("assistant")[-1].strip())
+```
 
 ## 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. -->
+The model was trained on a preference dataset generated by the **Iteration 1 model** (`NilayR/llama32-iterative-dpo-iter1`).
 
-[More Information Needed]
+  * **Data Generation Process:**
+    1.  **Instructions:** The model from Iteration 1 generated responses to 20 instructions from the LIMA dataset.
+    2.  **Preference Labeling:** A custom LLM Judge powered by `GPT-3.5-Turbo` evaluated pairs of the new responses, creating a dataset of **57 chosen/rejected pairs**.
 
 ### 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]
-
+The model was trained for one epoch using the TRL library's `DPOTrainer`.
 
 #### 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
+  * **Framework:** `trl.DPOTrainer`
+  * **Epochs:** 1
+  * **Batch Size:** 1
+  * **Gradient Accumulation Steps:** 2 (Effective Batch Size: 2)
+  * **Optimizer:** `paged_adamw_8bit`
+  * **Learning Rate:** 2e-5
+  * **DPO Beta (β):** 0.1
+  * **Max Steps:** 50
+  * **Final Training Loss:** `0.6343`
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+#### LoRA Configuration
 
-[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]
+  * **Rank (`r`):** 16
+  * **Alpha (`lora_alpha`):** 32
+  * **Target Modules:** `q_proj`, `k_proj`, `v_proj`, `o_proj`
+  * **Dropout:** 0.05
 
 ### 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]
+  * **Hardware:** 1x NVIDIA A100 40GB GPU
+  * **Cloud Provider:** Google Colab
+  * **Software:** `transformers`, `peft`, `trl`, `bitsandbytes`
 
-[More Information Needed]
+-----
 
-## Model Card Contact
 
-[More Information Needed]
+```
+```