This is a causal language model fine‑tuned to generate conceptual explanations in the style of Richard Feynman, using supervised learning on curated prompt–completion pairs. It serves as a strong pedagogical baseline before any reinforcement learning stage.

Model Details

Model Description

“KhushalM/Qwen2.5-1.5-SFT-Merged” is built on top of the Qwen/Qwen2.5-1.5B-Instruct base. It was supervised‑finetuned on approximately 750 high‑quality prompt–completion pairs designed to replicate the Feynman teaching approach: simple, first‑principles explanations with concrete analogies, layered structure, and occasional comprehension checks. After finetuning, LoRA adapters were merged for streamlined inference.

Finetuned by: Khushal Mandavia
Model type: Decoder‑only transformer (causal LM)
Finetuned from: Qwen/Qwen2.5-1.5B-Instruct
Repository: https://huggingface.co/KhushalM/Qwen2.5-1.5-SFT-Merged

Uses

Direct Use

Conceptual Explanation: Generate analogy‑driven, step‑by‑step explanations of scientific and technical topics.
Educational Prototyping: Rapidly test teaching content formats or draft study materials.

Downstream Use

Warm‑start for RL: Serve as the supervised-learning base before reinforcement learning stages.
Fine‑tuned Deployment: Use in chatbots or tutoring systems requiring clear pedagogical style.

Out‑of‑Scope Use

High‑stakes Factual Tasks: Not designed for critical decision‑making without verification.
General Chat: Optimized for explanatory style rather than casual conversation.

Bias, Risks, and Limitations

The model produces engaging and clear explanations but may:

Overgeneralize: Simplifications can omit important nuances.
Hallucinate: Introduce plausible but incorrect statements.
Style Fixation: Rigidly adhere to the Feynman pattern even when another tone is preferable.

Recommendation: Always review outputs for accuracy and context relevance. Consider further domain‑specific finetuning where needed.

How to Get Started with the Model

python

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

model_id = "KhushalM/Qwen2.5-1.5-SFT-Merged"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", trust_remote_code=True)

generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    device="cuda"  # or device="cpu"
)

prompt = "Explain the concept of entropy from first principles."
output = generator(prompt, max_new_tokens=180, temperature=0.7, top_p=0.9)
print(output[0]["generated_text"])