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README.md

@@ -1,222 +1,222 @@
----
-base_model: Qwen/Qwen2.5-1.5B-Instruct
-library_name: peft
-license: mit
-language:
-  - en
-pipeline_tag: text-generation
-tags:
-  - promptforge
-  - prompt-optimization
-  - prompt-engineering
-  - lora
-  - peft
-  - qwen2.5
-  - text-generation
-  - base_model:adapter:Qwen/Qwen2.5-1.5B-Instruct
-  - transformers
----
-
-# 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
-
-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:** 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
-
-- **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
-
-### Direct Use
-
-- 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)
-
-Example weak → strong:
-
-| 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 |
-
-### Downstream Use
-
-- 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
-
-- 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
-
-- 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
-
-- 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
-
-### With PromptForge (recommended)
-
-```bash
-pip install promptforge
-# or from source: pip install -e ".[demo]"
-
-python -m promptforge download \
-  --quality-repo YOUR_HF_USERNAME/PromptForge-Quality \
-  --optimizer-repo YOUR_HF_USERNAME/PromptForge-Optimizer
-
-python -m promptforge run "Make an app about social media like facebook and stuff"
-```
-
-```python
-from promptforge import PromptForge
-
-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"])
-```
-
-### Load the adapter directly (PEFT)
-
-```python
-from peft import PeftModel
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-base = "Qwen/Qwen2.5-1.5B-Instruct"
-adapter = "YOUR_HF_USERNAME/PromptForge-Optimizer"
-
-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)
-```
-
-Use Qwen’s chat template (`tokenizer.apply_chat_template`) — do not hand-roll `<|system|>` tags.
-
-## Training Details
-
-### Training Data
-
-- **~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)
-
-### Training Procedure
-
-#### Training Hyperparameters
-
-| 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
-
-## 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 |
-
-Evaluation is primarily: held-out SFT loss + pipeline checks (score delta, instruction preservation, repetition detection). Not a public leaderboard benchmark.
-
-### Summary
-
-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
-
-- **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
-
-## Technical Specifications
-
-### Model Architecture and Objective
-
-- Causal language model (Qwen2.5 Instruct) + LoRA
-- Objective: SFT to map weak prompt (+ analysis) → optimized prompt text only
-
-### Compute Infrastructure
-
-#### Hardware
-
-- RTX 5060 Laptop GPU, 8 GB VRAM
-
-#### Software
-
-- PyTorch (CUDA)
-- Transformers
-- PEFT / LoRA
-- PromptForge training scripts
-
-### Framework versions
-
-- PEFT 0.20.0
-
-## Citation
-
-```bibtex
-@software{promptforge_optimizer,
-  title = {PromptForge-Optimizer},
-  author = {PromptForge Contributors},
-  year = {2026},
-  url = {https://huggingface.co/YOUR_HF_USERNAME/PromptForge-Optimizer}
-}
-```
-
-## Model Card Contact
-
-Open an issue on the PromptForge GitHub repository.
+---
+base_model: Qwen/Qwen2.5-1.5B-Instruct
+library_name: peft
+license: mit
+language:
+  - en
+pipeline_tag: text-generation
+tags:
+  - promptforge
+  - prompt-optimization
+  - prompt-engineering
+  - lora
+  - peft
+  - qwen2.5
+  - text-generation
+  - base_model:adapter:Qwen/Qwen2.5-1.5B-Instruct
+  - transformers
+---
+
+# 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/arjun988/promptModel) — local-first prompt quality scoring + optimization.
+
+## Model Details
+
+### Model Description
+
+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:** 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
+
+- **Repository:** https://github.com/arjun988/promptModel
+- **Companion model:** PromptForge-Quality (ModernBERT multi-dimension prompt scorer)
+- **Demo:** Gradio app in the PromptForge repo (`demo/app.py`)
+
+## Uses
+
+### Direct Use
+
+- 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)
+
+Example weak → strong:
+
+| 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 |
+
+### Downstream Use
+
+- 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
+
+- 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
+
+- 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
+
+- 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
+
+### With PromptForge (recommended)
+
+```bash
+pip install promptforge
+# or from source: pip install -e ".[demo]"
+
+python -m promptforge download \
+  --quality-repo ArjunShukla/PromptForge-Quality \
+  --optimizer-repo ArjunShukla/PromptForge-Optimizer
+
+python -m promptforge run "Make an app about social media like facebook and stuff"
+```
+
+```python
+from promptforge import PromptForge
+
+pf = PromptForge(
+    quality_model_path="ArjunShukla/PromptForge-Quality",
+    optimizer_model_path="ArjunShukla/PromptForge-Optimizer",
+)
+print(pf.run("Build me a website for a startup")["optimized_prompt"])
+```
+
+### Load the adapter directly (PEFT)
+
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+base = "Qwen/Qwen2.5-1.5B-Instruct"
+adapter = "ArjunShukla/PromptForge-Optimizer"
+
+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)
+```
+
+Use Qwen’s chat template (`tokenizer.apply_chat_template`) — do not hand-roll `<|system|>` tags.
+
+## Training Details
+
+### Training Data
+
+- **~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)
+
+### Training Procedure
+
+#### Training Hyperparameters
+
+| 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
+
+## 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 |
+
+Evaluation is primarily: held-out SFT loss + pipeline checks (score delta, instruction preservation, repetition detection). Not a public leaderboard benchmark.
+
+### Summary
+
+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
+
+- **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
+
+## Technical Specifications
+
+### Model Architecture and Objective
+
+- Causal language model (Qwen2.5 Instruct) + LoRA
+- Objective: SFT to map weak prompt (+ analysis) → optimized prompt text only
+
+### Compute Infrastructure
+
+#### Hardware
+
+- RTX 5060 Laptop GPU, 8 GB VRAM
+
+#### Software
+
+- PyTorch (CUDA)
+- Transformers
+- PEFT / LoRA
+- PromptForge training scripts
+
+### Framework versions
+
+- PEFT 0.20.0
+
+## Citation
+
+```bibtex
+@software{promptforge_optimizer,
+  title = {PromptForge-Optimizer},
+  author = {PromptForge Contributors},
+  year = {2026},
+  url = {https://huggingface.co/ArjunShukla/PromptForge-Optimizer}
+}
+```
+
+## Model Card Contact
+
+Open an issue on the PromptForge GitHub repository.