Update README.md

README.md

@@ -1,62 +1,230 @@
 ---
 base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
 library_name: peft
-model_name: taskmind_lora_peft
+model_name: TaskMind — TinyLlama 1.1B Chat LoRA
 tags:
-- base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0
-- lora
-- sft
-- transformers
-- trl
-licence: license
+  - lora
+  - sft
+  - peft
+  - trl
+  - transformers
+  - text-classification
+  - intent-detection
+  - task-management
+  - hinglish
+  - base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0
+license: apache-2.0
 pipeline_tag: text-generation
+language:
+  - en
+  - hi
+metrics:
+  - token_accuracy
 ---
 
-# Model Card for taskmind_lora_peft
+# TaskMind — TinyLlama 1.1B Chat LoRA
 
-This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0).
-It has been trained using [TRL](https://github.com/huggingface/trl).
+A LoRA adapter fine-tuned on [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) for **WhatsApp message intent classification and structured task extraction** in English and Hinglish (Hindi–English code-switch).
 
-## Quick start
+Trained entirely on **Apple Silicon MPS (M5 Max)** — no cloud GPU, no cost, 2 minutes 12 seconds.
+
+> 📦 Full pipeline, production API server, test suite, and deployment docs →
+> [github.com/vijendradhanotiya/taskmind-ai](https://github.com/vijendradhanotiya/taskmind-ai)
+
+---
+
+## What It Does
+
+Given a raw WhatsApp team message, the model extracts structured intent as JSON — the model itself outputs valid JSON, no regex hacks needed.
+
+**Input:**
+```
+@Neha the design review is pending from your end
+```
+
+**Output:**
+```json
+{
+  "intent": "TASK_ASSIGN",
+  "assigneeName": "Neha",
+  "project": null,
+  "title": "Design review",
+  "deadline": null,
+  "priority": "normal",
+  "progressPercent": null
+}
+```
+
+---
+
+## Supported Intents
+
+| Intent | Trigger Pattern | Example |
+|---|---|---|
+| `TASK_ASSIGN` | @mention + action | "@Rohan review the PR I just pushed" |
+| `TASK_DONE` | completion language | "done bhai, merged the PR" |
+| `TASK_UPDATE` | progress percentage | "login page 60% ho gaya" |
+| `TASK_BLOCKED` | blocker / error | "CI/CD pipeline is broken again" |
+| `PROGRESS_NOTE` | status update | "deployment failed on prod — rollback initiated" |
+| `GENERAL_MESSAGE` | no task signal | "good morning team!", "okay noted" |
+
+---
+
+## Quick Start
 
 ```python
-from transformers import pipeline
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch, json
+
+BASE_MODEL = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
+ADAPTER    = "SatyamSinghal/taskmind-1.1b-chat-lora"
+
+tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
+model     = AutoModelForCausalLM.from_pretrained(BASE_MODEL, torch_dtype=torch.float32)
+model     = PeftModel.from_pretrained(model, ADAPTER)
+model.eval()
+
+SYSTEM_PROMPT = (
+    "You are TaskMind, an AI that reads WhatsApp messages and extracts structured task data. "
+    "Always respond with valid JSON only. No explanation. No markdown."
+)
+
+def classify(message: str) -> dict:
+    chat = [
+        {"role": "system", "content": SYSTEM_PROMPT},
+        {"role": "user",   "content": message},
+    ]
+    ids = tokenizer.apply_chat_template(chat, return_tensors="pt", add_generation_prompt=True)
+    with torch.no_grad():
+        out = model.generate(ids, max_new_tokens=150, do_sample=False, pad_token_id=tokenizer.eos_token_id)
+    text = tokenizer.decode(out[0][ids.shape[-1]:], skip_special_tokens=True).strip()
+    try:
+        return json.loads(text)
+    except json.JSONDecodeError:
+        return {"raw": text, "parse_success": False}
+
+print(classify("@Agrim fix the growstreams deck ASAP"))
+```
+
+---
+
+## Training Details
+
+| Parameter | Value |
+|---|---|
+| Base model | TinyLlama/TinyLlama-1.1B-Chat-v1.0 |
+| Method | LoRA (Low-Rank Adaptation) via SFT |
+| LoRA rank | r = 16 |
+| LoRA alpha | 32 |
+| Target modules | q_proj, v_proj |
+| Trainable params | ~4.2M / 1.1B (0.38%) |
+| Dataset size | 131 training + 20 validation examples |
+| Epochs | 5 |
+| Batch size | 4 |
+| Max sequence length | 512 |
+| Optimizer | AdamW (paged) |
+| Learning rate | 2e-4 with cosine schedule |
+| Hardware | Apple M5 Max — MPS backend |
+| Training time | 2 minutes 12 seconds |
+| Training cost | $0 |
 
-question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
-generator = pipeline("text-generation", model="None", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
+---
+
+## Performance
+
+| Metric | Before Fine-tuning | After Fine-tuning |
+|---|---|---|
+| Eval loss | 2.28 | **0.39** |
+| Token accuracy | 59% | **92.8%** |
+| JSON parse success | ~30% | **~97%** |
+| Correct intent | Often wrong | **Correct in tested cases** |
+
+### Before vs After — Real Examples
+
+| Message | Base Model | TaskMind |
+|---|---|---|
+| `@Agrim fix deck ASAP` | Fake deadline 2021-01-01, assignee "John Doe" | `TASK_ASSIGN`, correct title |
+| `done bhai, merged the PR` | Fake project "PR-123", wrong intent | `TASK_DONE`, null fields |
+| `login page 60% ho gaya` | `TASK_ASSIGN`, hallucinated data | `TASK_UPDATE`, progressPercent=60 |
+| `getting 500 error` | Hallucinated task | `GENERAL_MESSAGE` |
+| `Sure sir ready for it` | John Doe, fake task | `GENERAL_MESSAGE`, null |
+
+---
+
+## API Server
+
+A production-ready FastAPI server wrapping this adapter is available in the companion repo.
+
+```bash
+git clone https://github.com/vijendradhanotiya/taskmind-ai
+pip install -r requirements.txt
+python3 -m uvicorn api.main:app --host 0.0.0.0 --port 8001
+```
+
+OpenAI-compatible endpoints included:
+
+```bash
+# Classify a WhatsApp message
+curl -X POST http://localhost:8001/v1/classify \
+  -H "Content-Type: application/json" \
+  -d '{"message": "@Vijendra deploy karo production pe aaj raat tak, urgent hai!"}'
+
+# Generic chat completion
+curl -X POST http://localhost:8001/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{"messages": [{"role": "user", "content": "What is LoRA?"}], "max_tokens": 150}'
 ```
 
-## Training procedure
+---
 
- 
+## Framework Versions
 
+| Library | Version |
+|---|---|
+| PEFT | 0.18.1 |
+| TRL | 1.1.0 |
+| Transformers | 4.57.0 |
+| PyTorch | 2.2.2 |
+| Datasets | 4.8.4 |
+| Tokenizers | 0.22.1 |
 
+---
 
-This model was trained with SFT.
+## Contributors
 
-### Framework versions
+| Name | Role | GitHub |
+|---|---|---|
+| **Satyam Singhal** | Model training, dataset curation, API development | [@SatyamSinghal](https://github.com/SatyamSinghal) |
+| **Vijendra Dhanotiya** | Architecture, deployment, repo maintainer | [@vijendradhanotiya](https://github.com/vijendradhanotiya) |
 
-- PEFT 0.18.1
-- TRL: 1.1.0
-- Transformers: 4.57.0
-- Pytorch: 2.2.2
-- Datasets: 4.8.4
-- Tokenizers: 0.22.1
+> Full source, deployment guide, hardware benchmarks, and test suite:
+> **[github.com/vijendradhanotiya/taskmind-ai](https://github.com/vijendradhanotiya/taskmind-ai)**
 
-## Citations
+---
 
+## Citation
 
+If you use this model or the TaskMind pipeline in your work:
+
+```bibtex
+@misc{taskmind2025,
+  title   = {TaskMind: WhatsApp Intent Classification via LoRA Fine-tuning on TinyLlama},
+  author  = {Singhal, Satyam and Dhanotiya, Vijendra},
+  year    = {2025},
+  url     = {https://huggingface.co/SatyamSinghal/taskmind-1.1b-chat-lora},
+  note    = {LoRA adapter for TinyLlama-1.1B-Chat-v1.0, trained on Apple Silicon MPS}
+}
+```
 
-Cite TRL as:
-    
 ```bibtex
 @software{vonwerra2020trl,
   title   = {{TRL: Transformers Reinforcement Learning}},
-  author  = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
+  author  = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward
+             and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif
+             and Gallouedec, Quentin},
   license = {Apache-2.0},
   url     = {https://github.com/huggingface/trl},
   year    = {2020}
 }
-```
+```