| --- |
| base_model: unsloth/Qwen2.5-72B-Instruct-bnb-4bit |
| tags: |
| - text-generation-inference |
| - transformers |
| - unsloth |
| - qwen2 |
| - trl |
| - resume |
| - career-coach |
| license: other |
| language: |
| - en |
| --- |
| |
| # 🧠 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 |
| - **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 |
|
|
| ## ⚠️ License & Usage Warning |
| **License:** **Tongyi Qianwen License Agreement (Research Only / Non-Commercial)** |
|
|
| 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 The Imaginator is designed to perform cognitive reasoning: it takes "lazy notes" or an "outdated resume," analyzes a specific target job (e.g., *Java Developer* pivoting to *DevOps*), and strategically reframes the candidate's experience to bridge skill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)) |
