| --- |
| 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)) |
| |
