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rudranshjoshi/
circuit

Name: circuit
Availability: InStock

microsoft/Phi-3-mini-4k-instruct

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Circuit

Fine-tuned Phi-3 for Logical Reasoning

Circuit Logo

Model performance

Benchmark

Trained on the lucasmccabe/logiqa dataset, Circuit enhances the model’s ability to reason through complex problems, answer multi-step logic questions, and provide consistent explanations.

Model Details

Property	Value
Base model	`microsoft/Phi-3-mini-4k-instruct`
Fine-tuned for	Logical Reasoning
Dataset	`lucasmccabe/logiqa`
Technique	LoRA fine-tuning, merged for direct use
Formats available	Full (HF Transformers) + Quantized (`.gguf` for llama.cpp / Ollama)
Project	Circuit
Fine-tuned by	Rudransh

Model Variants

Variant	Description	File
Full model	Merged LoRA with base, compatible with `transformers`	`pytorch_model.bin`
Quantized model (GGUF)	Optimized for CPU/GPU inference via `llama.cpp`, `text-generation-webui`, or `Ollama`	`circuit_phi3_q4.gguf`

Example Usage (Transformers)

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "rudranshjoshi/circuit",
    torch_dtype=torch.float16,
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(
    "rudranshjoshi/circuit",
    trust_remote_code=True
)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt").to(device)
outputs = model.generate(**inputs, max_new_tokens=150)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Summary

Base model: Phi-3 Mini 4K Instruct

Dataset: LogiQA (lucasmccabe/logiqa)

Training method: LoRA fine-tuning, later merged

Hardware: NVIDIA RTX 1080

Epochs: ~3

Objective: Improve reasoning consistency and structured explanations

Acknowledgements

Microsoft for Phi-3

Lucas McCabe for LogiQA dataset

Fine-tuned and quantized by Rudransh under Project Circuit