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vmal/
qwen2-7b-logical-reasoning

Name: qwen2-7b-logical-reasoning
Availability: InStock

vmal/ConfinityChatMLv1

Qwen/Qwen2-7B

adapter

apache-2.0

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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

Benchmark on your specific tasks before production use.
Human-in-the-loop review for high-stakes decisions.
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))