---
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))