Magnum-72B-Imaginator-LoRA

🧠 The Imaginator: Magnum-72B-Career-Strategist

<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>

• Developed by: jeff-calderon
• Base Model: 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 The Imaginator is designed to perform cognitive reasoning: it takes "lazy notes" or an "outdated resume," analyzes a specific target job (e.g., Java Developer pivoting to DevOps), and strategically reframes the candidate's experience to bridge skill gaps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)

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))