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README.md

@@ -1,207 +1,222 @@
 ---
 base_model: Qwen/Qwen2.5-1.5B-Instruct
 library_name: peft
+license: mit
+language:
+  - en
 pipeline_tag: text-generation
 tags:
-- base_model:adapter:Qwen/Qwen2.5-1.5B-Instruct
-- lora
-- transformers
+  - promptforge
+  - prompt-optimization
+  - prompt-engineering
+  - lora
+  - peft
+  - qwen2.5
+  - text-generation
+  - base_model:adapter:Qwen/Qwen2.5-1.5B-Instruct
+  - transformers
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
+# PromptForge-Optimizer
 
+LoRA adapter that rewrites **weak / vague prompts** into **clear, specific, actionable LLM prompts** while preserving the original intent and topic.
 
+Part of [PromptForge](https://github.com/YOUR_USER/promptModel) — local-first prompt quality scoring + optimization.
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-
+PromptForge-Optimizer is a **PEFT/LoRA** fine-tune of [`Qwen/Qwen2.5-1.5B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct). Given a weak user prompt (plus optional quality analysis context), it generates an improved prompt with audience, constraints, structure, and output format — without changing the core topic.
 
-- **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]
+- **Developed by:** PromptForge contributors
+- **Model type:** Causal LM adapter (LoRA / PEFT)
+- **Language(s):** English
+- **License:** MIT
+- **Finetuned from:** [`Qwen/Qwen2.5-1.5B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct)
 
-### Model Sources [optional]
+### Model Sources
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** https://github.com/YOUR_USER/promptModel
+- **Companion model:** PromptForge-Quality (ModernBERT multi-dimension prompt scorer)
+- **Demo:** Gradio app in the PromptForge repo (`demo/app.py`)
 
 ## 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. -->
+- Rewrite vague prompts into production-ready LLM instructions
+- Pair with **PromptForge-Quality** for score → optimize → re-score workflows
+- Local / offline prompt tooling (CLI, Python API, Gradio)
 
-[More Information Needed]
+Example weak → strong:
 
-### Downstream Use [optional]
+| Weak | Optimized (intent preserved) |
+|------|------------------------------|
+| `Make an app about social media like facebook and stuff` | Social media / Facebook-like app prompt with profiles, feed, likes, constraints, output format |
 
-<!-- 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]
+- Prompt engineering assistants
+- IDE / agent tooling that improves user instructions before calling an LLM
+- Synthetic data pipelines that need higher-quality prompts
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+- Not a general chat assistant
+- Not a substitute for domain experts (legal, medical, safety-critical advice)
+- Not guaranteed to preserve intent on topics far outside the curated training set
+- Do not use to generate harmful, deceptive, or disallowed content
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+- Trained on **curated synthetic** weak→strong pairs; coverage is strongest on coding apps, writing, data, research, and planning prompts
+- May invent plausible audience / stack details (e.g. “product managers”, “Flask”) when the weak prompt is underspecified
+- Small base model (**1.5B**) — quality is good for local use, not frontier-LLM rewrite quality
+- Inference includes validation + fallback in the PromptForge package; raw adapter output alone may still drift
 
 ### 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.
+- Prefer the **PromptForge Python package / CLI** (chat template + stop tokens + validation) over raw `generate`
+- For new domains, add your own weak→strong pairs and retrain the LoRA
+- Always review optimized prompts before sending them to production LLMs
 
 ## 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]
+### With PromptForge (recommended)
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+```bash
+pip install promptforge
+# or from source: pip install -e ".[demo]"
 
-[More Information Needed]
+python -m promptforge download \
+  --quality-repo YOUR_HF_USERNAME/PromptForge-Quality \
+  --optimizer-repo YOUR_HF_USERNAME/PromptForge-Optimizer
 
-## Evaluation
+python -m promptforge run "Make an app about social media like facebook and stuff"
+```
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+```python
+from promptforge import PromptForge
 
-### Testing Data, Factors & Metrics
+pf = PromptForge(
+    quality_model_path="YOUR_HF_USERNAME/PromptForge-Quality",
+    optimizer_model_path="YOUR_HF_USERNAME/PromptForge-Optimizer",
+)
+print(pf.run("Build me a website for a startup")["optimized_prompt"])
+```
 
-#### Testing Data
+### Load the adapter directly (PEFT)
 
-<!-- This should link to a Dataset Card if possible. -->
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-[More Information Needed]
+base = "Qwen/Qwen2.5-1.5B-Instruct"
+adapter = "YOUR_HF_USERNAME/PromptForge-Optimizer"
 
-#### Factors
+tokenizer = AutoTokenizer.from_pretrained(adapter, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(base, trust_remote_code=True, device_map="auto")
+model = PeftModel.from_pretrained(model, adapter)
+```
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+Use Qwen’s chat template (`tokenizer.apply_chat_template`) — do not hand-roll `<|system|>` tags.
 
-[More Information Needed]
+## Training Details
 
-#### Metrics
+### Training Data
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+- **~800** curated high-quality **weak → strong** prompt pairs
+- **~140** unique topic-preserving seeds (coding, writing, data, research, general)
+- Intent rule: optimized prompt must keep the same topic as the weak prompt
+- Assistant-only loss masking (system/user tokens not trained)
 
-[More Information Needed]
+### Training Procedure
 
-### Results
+#### Training Hyperparameters
 
-[More Information Needed]
+| Setting | Value |
+|---------|-------|
+| Base model | `Qwen/Qwen2.5-1.5B-Instruct` |
+| Method | LoRA (PEFT) |
+| LoRA rank / alpha | 16 / 32 |
+| Target modules | q/k/v/o + MLP projections |
+| Max sequence length | 512 |
+| Epochs | 6 |
+| Effective batch size | 8 (batch 1 × grad accum 8) |
+| Learning rate | 1e-4 |
+| Precision | fp16 |
+| Gradient checkpointing | enabled |
+| Config | `configs/optimizer_fast_8gb.yaml` |
+
+#### Speeds, Sizes, Times
+
+- **Hardware:** NVIDIA GeForce RTX 5060 Laptop GPU (8 GB)
+- **Wall time:** ~87 minutes (6 epochs)
+- **Adapter size on disk:** ~82 MB
+- **Train loss:** ~0.47
+- **Validation loss:** ~0.121
 
-#### Summary
+## Evaluation
 
+### Metrics
 
+| Signal | Result |
+|--------|--------|
+| Validation loss | **0.121** |
+| Example quality lift (scorer) | e.g. **41.5 → 94.0** on a social-media app prompt |
+| Intent preservation | Topic keywords retained (social / Facebook) |
+| Validation gate | Rejects empty / repetitive / low-intent outputs |
 
-## Model Examination [optional]
+Evaluation is primarily: held-out SFT loss + pipeline checks (score delta, instruction preservation, repetition detection). Not a public leaderboard benchmark.
 
-<!-- Relevant interpretability work for the model goes here -->
+### Summary
 
-[More Information Needed]
+The adapter reliably expands vague prompts into structured instructions on in-distribution topics. Off-distribution prompts may fall back to a safer template when used through PromptForge.
 
 ## 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:** NVIDIA RTX 5060 Laptop (8 GB)
+- **Hours used:** ~1.5 h for this adapter run
+- **Cloud Provider:** N/A (local)
+- **Compute Region:** N/A
+- **Carbon Emitted:** Not measured
 
-- **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]
+## Technical Specifications
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+- Causal language model (Qwen2.5 Instruct) + LoRA
+- Objective: SFT to map weak prompt (+ analysis) → optimized prompt text only
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
 #### Hardware
 
-[More Information Needed]
+- RTX 5060 Laptop GPU, 8 GB VRAM
 
 #### Software
 
-[More Information Needed]
-
-## Citation [optional]
+- PyTorch (CUDA)
+- Transformers
+- PEFT / LoRA
+- PromptForge training scripts
 
-<!-- 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]
+### Framework versions
 
-[More Information Needed]
+- PEFT 0.20.0
 
-## Model Card Authors [optional]
+## Citation
 
-[More Information Needed]
+```bibtex
+@software{promptforge_optimizer,
+  title = {PromptForge-Optimizer},
+  author = {PromptForge Contributors},
+  year = {2026},
+  url = {https://huggingface.co/YOUR_HF_USERNAME/PromptForge-Optimizer}
+}
+```
 
 ## Model Card Contact
 
-[More Information Needed]
-### Framework versions
-
-- PEFT 0.20.0
+Open an issue on the PromptForge GitHub repository.