vmal
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qwen2-7b-logical-reasoning

@@ -1,202 +1,96 @@
 ---
 base_model: Qwen/Qwen2-7B
 library_name: peft
 ---
-# Model Card for Model ID
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-### Framework versions
-- PEFT 0.15.2

 ---
+license: apache-2.0
 base_model: Qwen/Qwen2-7B
 library_name: peft
+datasets:
+- vmal/ConfinityChatMLv1
+tags:
+- logical-reasoning
+- chain-of-thought
+- lora
+- peft
+- conversational
 ---
+## Overview
+An autoregressive language model fine-tuned on ConfinityChatMLv1 for enhanced chain-of-thought and logical reasoning in conversational settings.
+Built on Qwen2-7B using PEFT/LoRA.
+---
 ## Model Details
+- **Base model:** Qwen/Qwen2-7B
+- **Library:** PEFT (LoRA)
+- **Model type:** Causal autoregressive transformer (decoder-only)
+- **Languages:** English (primary)
+- **License:** Apache-2.0 (inherits Qwen2-7B license)
+- **Finetuned from:** Qwen/Qwen2-7B
+- **Repository:** https://huggingface.co/vmal/qwen2-7b-logical-reasoning
+- **Dataset:** ConfinityChatMLv1 (~140K reasoning dialogues)
+---
 ## Uses
 ### Direct Use
+- Provide step-by-step solutions to logic puzzles & math word problems
+- Assist with structured reasoning in chatbots & virtual tutors
+- Generate chain-of-thought–style explanations alongside answers
+### Downstream Use
+- Automated grading & feedback on student solutions
+- Knowledge-graph population via inference chains
+- Hybrid QA systems requiring explanation traces
+### Out-of-Scope
+- Creative/open-ended story generation
+- Highly domain-specific expert systems without further fine-tuning
+- Low-latency real-time deployment on edge devices
+---
+## Bias, Risks & Limitations
+- **Inherited biases:** Cultural and gender stereotypes from pretraining corpus
+- **Hallucinations:** May produce unsupported or incorrect facts when outside training scope
+- **Overconfidence:** Can present flawed reasoning as fact, especially on adversarial or OOD tasks
 ### Recommendations
+1. **Benchmark** on your specific tasks before production use.
+2. **Human-in-the-loop** review for high-stakes decisions.
+3. **Ground outputs** with retrieval systems for verifiable sources.
+---
+## Quick Start
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+# Load tokenizer & base model
+tokenizer = AutoTokenizer.from_pretrained(
+    "vmal/qwen2-7b-logical-reasoning",
+    trust_remote_code=True
+)
+base = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen2-7B",
+    trust_remote_code=True,
+    device_map="auto"
+)
+# Load LoRA adapters
+model = PeftModel.from_pretrained(base, "vmal/qwen2-7b-logical-reasoning")
+# Inference example
+prompt = (
+    "Solve step by step: If all bloops are razzies, and some razzies are lazzies, "
+    "are all bloops lazzies?"
+)
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=256)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))