language, pipeline_tag, library_name, license, base_model, tags, model-index

language	pipeline_tag	library_name	license	base_model	tags	model-index
en	text-generation	transformers	apache-2.0	Qwen/Qwen2.5-3B-Instruct	qwen2.5 merged-model sci-fi instruction-tuning educational	name results hail-mary-inspired-student-merged

Hail Mary Inspired Student (Merged)

This is a merged, full-weight model produced from:

• Base model: Qwen/Qwen2.5-3B-Instruct
• LoRA adapter: Stinger2311/hail-mary-inspired-student-lora

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)

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.