Update README.md

README.md

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----
-license: apache-2.0
----
+---
+language:
+- en
+license: apache-2.0
+tags:
+- mistral
+- causal-lm
+- text-generation
+- qlora
+- merged-lora
+- mathematics
+- logic
+- principia-mathematica
+- research
+pipeline_tag: text-generation
+base_model: mistralai/Mistral-7B-v0.1
+model_type: mistral
+library_name: transformers
+model_creator: clarkkitchen22
+---
+
+# 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.
+
+---
+
+## 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  
+
+---
+
+## 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
+
+---
+
+## 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.
+
+---
+
+## How to Use
+
+### Basic loading (Transformers)
+
+```python
+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))
+
+
+
+
+---
+license: apache-2.0
+---