PMCommunicator

PMCommunicator is a LoRA fine-tune of Phi-3.5-mini-instruct (3.8B parameters) specialized for generating professional project management communications.

Given a project context (from PMPlanner + PMReasoner), it generates stakeholder-ready prose: kickoff emails, status reports, risk escalation memos, executive summaries, board updates, and project closeout reports.

Model Details

Property	Value
Base model	microsoft/Phi-3.5-mini-instruct (3.8B)
Fine-tuning method	LoRA (PEFT)
LoRA rank	16, alpha 32
Trainable params	25M / 3.82B (0.65%)
Training data	28,000+ PM communication examples
Val loss	0.0105
License	MIT

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "pmcore/pmcommunicator"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")

system = (
    "You are PMCommunicator, an expert project manager and communications specialist. "
    "Generate professional, stakeholder-ready project communications based on the provided "
    "project context. Be specific — use the actual project name, numbers, and timeline. "
    "Write in clear business English. Output only the communication document itself."
)
user = "Project: Cloud migration, 50 legacy apps, 18 months, $8M budget. 3 phases planned.\n\nWrite a weekly status report for stakeholders."

prompt = f"<|system|>\n{system}<|end|>\n<|user|>\n{user}<|end|>\n<|assistant|>\n"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.3, do_sample=True)
print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

Full Pipeline

Use PMCommunicator as part of the full PMCore pipeline for best results. See PMCore on GitHub.