Update README.md

README.md

@@ -6,17 +6,95 @@ tags:
 - unsloth
 - qwen2
 - trl
-license: apache-2.0
+- resume
+- career-coach
+license: other
 language:
 - en
 ---
 
-# Uploaded  model
+# 🧠 The Imaginator: Magnum-72B-Career-Strategist
+
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
 
 - **Developed by:** jeff-calderon
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/Qwen2.5-72B-Instruct-bnb-4bit
+- **Base Model:** [unsloth/Qwen2.5-72B-Instruct-bnb-4bit](https://huggingface.co/unsloth/Qwen2.5-72B-Instruct-bnb-4bit)
+- **Finetuned from model:** unsloth/Qwen2.5-72B-Instruct-bnb-4bit
+- **Fine-tuning Framework:** Unsloth / QLoRA
 
-This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth)
+## ⚠️ License & Usage Warning
+**License:** **Tongyi Qianwen License Agreement (Research Only / Non-Commercial)**
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+This model is derived from Qwen-72B. Users must comply with the original Alibaba Cloud Tongyi Qianwen License Agreement. This model is intended for **research and educational purposes only**.
+
+---
+
+## 🎯 The Vision
+We set out to build **"The Imaginator"**—not just a generic resume writer, but a high-level **Career Strategist**. 
+
+Most AI resume tools simply fix grammar. The Imaginator is designed to perform cognitive reasoning: it takes "lazy notes" or an "outdated resume," analyzes a specific target job (e.g., *Java Developer* pivoting to *DevOps*), and strategically reframes the candidate's experience to bridge skill gaps without fabricating history.
+
+## 🏗️ The "Trinity" Dataset Strategy
+To achieve this level of reasoning, we rejected standard freelance datasets (which often sound like sales pitches). Instead, we engineered a custom "Trinity Dataset" of **7,417 high-quality records** via a local data factory on an RTX 4080:
+
+### 1. Type A: The Stylist (Tone & Impact)
+*   **Goal:** Master professional, metric-driven business English.
+*   **Input:** Weak, passive bullet points.
+*   **Output:** Powerful "STAR" method achievements (Situation, Task, Action, Result).
+*   **Source:** Mined 2,000 real resume bullets and utilized Grok to inject industry-standard metrics.
+
+### 2. Type B: The Strategist (Logic & Pivoting)
+*   **Goal:** Strategic Reframing.
+*   **Input:** A complex JSON payload containing Candidate Context + Target Job + Identified Skill Gaps.
+*   **Output:** A rewritten experience section that "bridges the gap" using transferable skills.
+*   **Method:** Simulated 1,000 career pivot scenarios (e.g., *Frontend Dev* $\to$ *Full Stack*) using Perplexity/Grok to ensure market accuracy.
+*   **Safety:** Rigorously filtered to ensure the model **never** invents fake job titles or promotions.
+
+### 3. Type C: The Creator (Synthesis from Chaos)
+*   **Goal:** Structuring unstructured data.
+*   **Input:** "Lazy" user brain dumps (lowercase, no formatting, typos).
+*   **Output:** Fully formatted, perfectly structured resume sections.
+*   **Source:** We used a "Ruiner Script" on 3,000 high-quality resumes to reverse-engineer them into lazy text messages, teaching the model how to reconstruct them.
+
+## 🚀 Capabilities & Performance
+
+This model was fine-tuned on an A100 GPU using Unsloth. It excels at:
+*   **Format Synthesis:** Turning raw text into polished documents.
+*   **Strategic Pivoting:** Rewriting experience to target specific roles.
+*   **Hallucination Control:** Trained specifically *not* to invent fake job titles to fill gaps.
+
+### Inference Example
+
+**Input (Lazy User):**
+> "i worked at amazon as a warehouse guy... hit rates... trained new people"
+
+**Imaginator Output:**
+> **Logistics Associate** | Amazon
+> *Packed products in a timely manner and consistently met or exceeded productivity rates. Trained and mentored new employees on safety protocols and packing procedures, improving team efficiency.*
+
+## 💻 How to Use (Unsloth)
+
+```python
+from unsloth import FastLanguageModel
+import torch
+
+max_seq_length = 8192
+dtype = None
+load_in_4bit = True
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = "jeff-calderon/Magnum-72B-Imaginator-LoRA", # Your model name here
+    max_seq_length = max_seq_length,
+    dtype = dtype,
+    load_in_4bit = load_in_4bit,
+)
+FastLanguageModel.for_inference(model)
+
+messages = [
+    {"role": "system", "content": "You are a professional resume writer. Convert the user's raw notes into a polished Experience section."},
+    {"role": "user", "content": "managed a team of 5 sales guys. we hit 1m in revenue."}
+]
+inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")
+
+outputs = model.generate(input_ids=inputs, max_new_tokens=512, use_cache=True, temperature=0.3)
+print(tokenizer.batch_decode(outputs[0], skip_special_tokens=True))