PrincipiaMistralModel7B

PrincipiaMistralModel7B is a 7B-parameter causal language model based on Mistral-7B-v0.1, fine-tuned via QLoRA on a custom corpus of logic- and math-focused text inspired by Principia Mathematica and related foundational material.

The goal of this model is to bias Mistral-7B toward:

• More formal reasoning about implications and basic proof structures
• Better familiarity with symbolic logic notation
• Explanations of classical foundations-of-mathematics ideas in clear English

This checkpoint is a fully merged model (LoRA merged into base), so it can be loaded directly with AutoModelForCausalLM without PEFT.

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Model Details

• Base model: mistralai/Mistral-7B-v0.1
• Architecture: Transformer (GQA + sliding window attention, as in Mistral-7B)
• Parameters: ~7B
• Library: Hugging Face transformers
• Finetuning method: QLoRA (low-rank adapters, later merged into full weights)
• Precision: Saved as safetensors sharded across 3 files

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Intended Use

Primary use cases

• Educational / research exploration of:

  • Basic propositional logic (e.g. implications, modus ponens, simple derivations)
  • Foundations-of-mathematics style narratives (inspired by Principia Mathematica)
  • Explanations of logic and proof ideas for students or hobbyists

• As a component model inside agents/tools that:

  • Need slightly more structured, formal reasoning than a generic base model
  • Work with simple proof sketches, logical implications, or math-adjacent text

Not intended for

• High-stakes decision making (finance, medicine, law, safety-critical systems)
• Use as a fully robust automated theorem prover
• Use without human oversight in any domain that affects real people’s lives

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Training & Data (High Level)

• Method: QLoRA fine-tuning on top of mistralai/Mistral-7B-v0.1, then weights merged.
• Hardware: Single consumer GPU (e.g., NVIDIA RTX 2070-class)
• Epochs: ~1 epoch over the custom dataset (light, targeted fine-tune)
• Data:
  • Text inspired by Principia Mathematica–style logic and foundational mathematics
  • Simple logical implication examples and step-by-step reasoning prompts
  • Explanations of core foundational concepts in natural language

This is a research/learning project, not a benchmark-optimized or industrially aligned model.

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How to Use

Basic loading (Transformers)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "clarkkitchen22/PrincipiaMistralModel7B"

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

prompt = (
    "We work in a simple propositional calculus.\n\n"
    "Premises:\n"
    "  (1) p -> q\n"
    "  (2) q -> r\n"
    "Conclusion:\n"
    "  (3) p -> r\n\n"
    "Explain, step by step, why (3) follows from (1) and (2)."
)

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=160,
        do_sample=True,
        top_p=0.9,
        temperature=0.3,
        repetition_penalty=1.15,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))




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license: apache-2.0
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