Sync LoRA adapter: README.md

README.md

@@ -1,96 +1,207 @@
 ---
 base_model: HuggingFaceTB/SmolLM2-360M-Instruct
 library_name: peft
-model_name: NeuralAI
-model_type: adapter
-license: apache-2.0
-language:
-- en
+pipeline_tag: text-generation
 tags:
-- text-generation
-- dpo
+- base_model:adapter:HuggingFaceTB/SmolLM2-360M-Instruct
 - lora
-- peft
-- smollm2
-- reasoning
-- code-generation
-- debugging
-- multi-step-reasoning
-- edge-ai
-pipeline_tag: text-generation
-inference:
-  parameters:
-    max_new_tokens: 512
-    temperature: 0.7
-    top_p: 0.95
-    repetition_penalty: 1.1
+- transformers
 ---
 
-# NeuralAI v15.0 — DPO-Aligned LoRA Adapter
-
-NeuralAI is a DPO-aligned LoRA adapter for [SmolLM2-360M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-360M-Instruct), fine-tuned for expert-level reasoning, code generation, debugging, and multi-step logic tasks.
-
-## Highlights
-
-- **597 DPO preference pairs** covering code correctness, logic, reasoning, debugging, and multi-step tasks
-- **Reward margin**: improved from ~0.5 to ~3.5 (model strongly prefers chosen responses)
-- **Final training loss**: 0.305
-- **Edge-optimized**: Runs on CPU with 4GB RAM — no GPU required
-- **Gemini-style alignment**: Helpful, structured, conversational tone with step-by-step explanations
-
-## Quick start
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from peft import PeftModel
-
-base_model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolLM2-360M-Instruct")
-tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM2-360M-Instruct")
-model = PeftModel.from_pretrained(base_model, "Subject-Emu-5259/NeuralAI")
-
-messages = [{"role": "user", "content": "Write a Python function to check API health."}]
-inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
-output = model.generate(inputs, max_new_tokens=256, temperature=0.7, top_p=0.95)
-print(tokenizer.decode(output[0][inputs.shape[-1]:], skip_special_tokens=True))
-```
-
-## Training details
-
-| Parameter | Value |
-|---|---|
-| Base model | HuggingFaceTB/SmolLM2-360M-Instruct |
-| Method | DPO (Direct Preference Optimization) |
-| Dataset | 597 preference pairs (v15 expanded) |
-| Epochs | 3 |
-| Steps | 450 |
-| Final loss | 0.305 |
-| Reward margin | ~3.5 |
-| LoRA rank | 16 |
-| Hardware | Apple Silicon MPS (MacBook Air M4) |
-| Duration | ~12 minutes |
-| Completed | 2026-07-11 |
-
-## Framework versions
-
-- PEFT: 0.17.1
-- TRL: 0.24.0
-- Transformers: 4.57.6
-- PyTorch: 2.8.0
-
-## Use cases
-
-- **Code generation and debugging**: Multi-step reasoning for code correctness
-- **Logic and math**: Complex problem decomposition
-- **Edge deployment**: CPU-optimized for local/private AI
-- **Agentic workflows**: Tool-use and multi-step task execution
-
-## Citation
-
-```bibtex
-@inproceedings{rafailov2023direct,
-    title        = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
-    author       = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
-    year         = 2023,
-    booktitle    = {NeurIPS 2023},
-}
-```
+# 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:**
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+[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 0.19.0