The objective is a calm, science-literate assistant style inspired by first-contact and high-uncertainty problem-solving themes, trained on original (public-safe) instruction data.

Intended use

Educational demos and portfolio projects
Prompted reasoning and explanation tasks
Lightweight experiments with a themed assistant persona

Not intended use

High-stakes decisions (medical, legal, safety-critical)
Claims of factual authority without external verification
Any official franchise affiliation or licensed reproduction

Quickstart (Transformers)

python

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "Stinger2311/hail-mary-inspired-student-merged"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    dtype=torch.float16,
    device_map="auto",
)

prompt = (
    "System: You are a calm, science-literate assistant. "
    "Be explicit about uncertainty when evidence is incomplete.\n\n"
    "User: How should a crew handle uncertainty during first contact?\n\n"
    "Assistant:"
)

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
    out = model.generate(
        **inputs,
        max_new_tokens=180,
        temperature=0.7,
        top_p=0.9,
        do_sample=True,
    )

reply_ids = out[0][inputs["input_ids"].shape[-1]:]
print(tokenizer.decode(reply_ids, skip_special_tokens=True))

Prompting tips

Use an explicit system instruction for tone and uncertainty handling.
Ask for structured outputs when you need consistency.
For safer behavior, request assumptions and confidence levels in the response.

Training lineage

This merged model comes from a LoRA fine-tuning workflow using:

Original seed + synthetic reviewed instruction data
Unsloth/QLoRA style tuning workflow
Adapter merge into full weights for easier deployment

Related assets:

Adapter model: Stinger2311/hail-mary-inspired-student-lora
Dataset: Stinger2311/hail-mary-inspired-sci-fi-instruct
Source repo: https://github.com/Chandan062311/Hail-Mary

Limitations

May hallucinate scientific details.
Performance depends heavily on prompt quality.
Not benchmarked for safety-critical production use.

Safety note

Outputs should be reviewed by a human before use in consequential contexts. For public demos, present this model as an educational themed assistant, not an authority system.