105 lines
2.9 KiB
Markdown
105 lines
2.9 KiB
Markdown
---
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language:
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- en
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pipeline_tag: text-generation
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library_name: transformers
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license: apache-2.0
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base_model: Qwen/Qwen2.5-3B-Instruct
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tags:
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- qwen2.5
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- merged-model
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- sci-fi
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- instruction-tuning
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- educational
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model-index:
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- name: hail-mary-inspired-student-merged
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results: []
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---
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# Hail Mary Inspired Student (Merged)
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This is a merged, full-weight model produced from:
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- Base model: `Qwen/Qwen2.5-3B-Instruct`
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- LoRA adapter: `Stinger2311/hail-mary-inspired-student-lora`
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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.
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## Intended use
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- Educational demos and portfolio projects
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- Prompted reasoning and explanation tasks
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- Lightweight experiments with a themed assistant persona
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## Not intended use
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- High-stakes decisions (medical, legal, safety-critical)
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- Claims of factual authority without external verification
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- Any official franchise affiliation or licensed reproduction
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## Quickstart (Transformers)
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "Stinger2311/hail-mary-inspired-student-merged"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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dtype=torch.float16,
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device_map="auto",
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)
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prompt = (
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"System: You are a calm, science-literate assistant. "
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"Be explicit about uncertainty when evidence is incomplete.\n\n"
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"User: How should a crew handle uncertainty during first contact?\n\n"
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"Assistant:"
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)
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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with torch.no_grad():
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out = model.generate(
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**inputs,
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max_new_tokens=180,
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temperature=0.7,
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top_p=0.9,
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do_sample=True,
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)
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reply_ids = out[0][inputs["input_ids"].shape[-1]:]
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print(tokenizer.decode(reply_ids, skip_special_tokens=True))
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```
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## Prompting tips
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- Use an explicit system instruction for tone and uncertainty handling.
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- Ask for structured outputs when you need consistency.
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- For safer behavior, request assumptions and confidence levels in the response.
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## Training lineage
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This merged model comes from a LoRA fine-tuning workflow using:
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- Original seed + synthetic reviewed instruction data
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- Unsloth/QLoRA style tuning workflow
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- Adapter merge into full weights for easier deployment
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Related assets:
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- Adapter model: `Stinger2311/hail-mary-inspired-student-lora`
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- Dataset: `Stinger2311/hail-mary-inspired-sci-fi-instruct`
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- Source repo: `https://github.com/Chandan062311/Hail-Mary`
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## Limitations
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- May hallucinate scientific details.
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- Performance depends heavily on prompt quality.
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- Not benchmarked for safety-critical production use.
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## Safety note
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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.
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