Sandeep4235/llama3-2-lora-wisdom

🏛️ Llama-3.2-3B-Instruct: Wisdom & Strategic Reasoning (LoRA)

Sandeep4235/llama3-2-lora-wisdom is a specialized Parameter-Efficient Fine-Tuning (PEFT) LoRA adapter trained on top of Meta Llama 3.2 3B Instruct.

This model is engineered to elevate standard conversational AI into a thoughtful, nuanced deliberative partner. Rather than providing generic listicles or evasive answers, it synthesizes classical philosophical doctrines (Stoicism, Aristotelian virtue ethics, Eastern non-dual philosophies, Pragmatism) with modern decision theory to offer deep contextual advice, ethical resolution frameworks, and strategic clarity.

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🌟 Executive Summary & Value Proposition

Standard instruction-tuned models frequently exhibit two failure modes when queried about life decisions, ethics, or leadership crises:

1. Superficial neutrality: Defaulting to non-committal clichés ("On one hand, this; on the other hand, that").
2. Brittle platitudes: Offering motivational tropes without analytical rigor or operational empathy.

Why 3B Parameters with LoRA?

• Edge & On-Device Native: At 3.21B parameters, the model requires under 2.2 GB of VRAM in 4-bit precision, making it deployable on consumer laptops, Apple Silicon MacBooks (M1/M2/M3/M4 via Ollama/GGUF), Raspberry Pi 5 clusters, or mobile edge environments.
• Low Latency Deliberation: Generates responses at 80–120+ tokens/second on a single RTX 3060/4060 GPU, enabling instantaneous multi-turn advisory sessions.
• Preserved World Knowledge: By keeping the base base weights intact and training a focused low-rank adapter across all linear projections (q, k, v, o, gate, up, down), the model maintains broad factual competence while fundamentally shifting its analytical tone and reasoning depth.

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🔬 Technical Specifications

Parameter	Specification
Model Name	Sandeep4235/llama3-2-lora-wisdom
Base Model	unsloth/Llama-3.2-3B-Instruct-unsloth-bnb-4bit / meta-llama/Llama-3.2-3B-Instruct
Architecture	Llama 3.2 Transformer Decoder with RoPE, Grouped-Query Attention (GQA)
Total Base Parameters	3.21 Billion
Trainable LoRA Parameters	28,311,552 (~0.88% of total model capacity)
Precision	4-bit NF4 (NormalFloat4) with bfloat16 compute activations
Context Window (Supported)	128,000 tokens
Training Sequence Length	2,048 tokens
LoRA Rank ($r$)	16
LoRA Alpha ($\alpha$)	16 (Scaling factor = 1.0)
LoRA Dropout	0 (Optimized via Unsloth kernel fusion)
Target Modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Training Framework	Unsloth + Hugging Face TRL (SFTTrainer)
Hardware Used	1x NVIDIA Tesla T4 / A100

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💬 Prompt Template & Formatting

This model adheres strictly to the official Llama 3.2 Instruct ChatML template:

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

You are an advisor possessing deep philosophical discernment, strategic clarity, and timeless wisdom. Balance ancient stoic, eastern, and modern ethical perspectives to provide actionable guidance.<|eot_id|><|start_header_id|>user<|end_header_id|>

{YOUR_QUERY_HERE}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

Recommended System Prompt: "You are an advisor possessing deep philosophical discernment, strategic clarity, and timeless wisdom. Balance ancient stoic, eastern, and modern ethical perspectives to provide actionable guidance."

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💻 Quickstart Inference Code

1. Ultra-Fast Execution via Unsloth (Recommended)

Unsloth delivers up to 2x faster inference and 70% lower VRAM usage compared to vanilla implementations:

import torch
from unsloth import FastLanguageModel

max_seq_length = 2048
dtype = None # Auto-detect GPU dtype (bfloat16 for Ampere+, float16 for Turing/Pascal)
load_in_4bit = True

# 1. Load the model and tokenizer
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "Sandeep4235/llama3-2-lora-wisdom",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
)

# 2. Enable native fast inference kernels
FastLanguageModel.for_inference(model)

# 3. Format conversational messages
messages = [
    {
        "role": "system",
        "content": "You are an advisor possessing deep philosophical discernment, strategic clarity, and timeless wisdom."
    },
    {
        "role": "user",
        "content": "How should a person reconcile the inevitability of human mortality with the daily pursuit of ambitious long-term goals?"
    }
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize = True,
    add_generation_prompt = True,
    return_tensors = "pt",
).to("cuda")

# 4. Generate response
outputs = model.generate(
    input_ids = inputs,
    max_new_tokens = 512,
    use_cache = True,
    temperature = 0.6,
    top_p = 0.9,
)

response = tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True)
print(response)

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2. Standard Inference via Hugging Face transformers + peft

If you are not using Unsloth, you can load the adapter on top of the base model using vanilla Hugging Face libraries:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel

base_model_id = "meta-llama/Llama-3.2-3B-Instruct"
lora_model_id = "Sandeep4235/llama3-2-lora-wisdom"

# Configure 4-bit quantization for minimal VRAM footprint
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load tokenizer and base model
tokenizer = AutoTokenizer.from_pretrained(base_model_id)
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    quantization_config=bnb_config,
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

# Attach LoRA adapter
model = PeftModel.from_pretrained(base_model, lora_model_id)
model.eval()

messages = [
    {
        "role": "system",
        "content": "You are an advisor possessing deep philosophical discernment, strategic clarity, and timeless wisdom."
    },
    {
        "role": "user",
        "content": "When evaluating an ethical conflict between duty to family and duty to society, how should one weigh the competing imperatives?"
    }
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=512,
        temperature=0.6,
        top_p=0.9,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id,
    )

input_len = inputs["input_ids"].shape[1]
print(tokenizer.decode(outputs[0][input_len:], skip_special_tokens=True))

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🦙 Local Deployment via Ollama & GGUF

To run this model on Ollama, LM Studio, or llama.cpp on your local workstation without writing Python code:

Step 1: Export Adapter to GGUF using Unsloth

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "Sandeep4235/llama3-2-lora-wisdom",
    max_seq_length = 2048,
    load_in_4bit = True,
)

# Export to 4-bit Medium Quantization (Q4_K_M)
model.save_pretrained_gguf(
    "llama3-2-wisdom-gguf",
    tokenizer,
    quantization_method = "q4_k_m"
)

Step 2: Create an Ollama Modelfile

Create a file named Modelfile in the export directory:

FROM ./llama3-2-wisdom-gguf/unsloth.Q4_K_M.gguf

TEMPLATE """{{ if .System }}<|start_header_id|>system<|end_header_id|>

{{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|>

{{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|>

{{ .Response }}<|eot_id|>"""

SYSTEM """You are an advisor possessing deep philosophical discernment, strategic clarity, and timeless wisdom."""

PARAMETER temperature 0.6
PARAMETER top_p 0.9
PARAMETER stop "<|eot_id|>"
PARAMETER stop "<|begin_of_text|>"

Step 3: Register & Run in Ollama

# Build the model inside Ollama
ollama create llama3.2-wisdom -f Modelfile

# Start an interactive advisory conversation
ollama run llama3.2-wisdom "What is the relationship between voluntary hardship and character development?"

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🎯 Intended Use & Applications

Domain Fit

• Executive & Leadership Advisory: Evaluating organizational decisions under intense uncertainty.
• Complex Life Crossroads: Career transitions, ethical dilemmas, balancing personal passion vs. pragmatic stability.
• Philosophical Inquiry: Explorations of Stoicism, existentialism, moral philosophy, and dialectics.
• Creative Writing & Persona Ingestion: Roleplaying as an elder mentor, sage, or strategic advisor in interactive narratives.

Out-of-Scope & Misuse

• Clinical Psychological Care: This model is not a licensed therapist, psychologist, or crisis counseling substitute. It must not be deployed to handle acute psychological distress or self-harm interventions.
• Binding Legal or Financial Advice: Does not replace certified financial planners, fiduciary advisors, or legal counsel.

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⚖️ Limitations & Ethical Considerations

1. Hallucination & Historical Attribution: While fine-tuned to extract philosophical concepts accurately, the model may occasionally misattribute specific quotes or aphorisms to historical figures.
2. Value Pluralism: Philosophical wisdom is subjective and culturally conditioned. While the model balances multiple philosophical traditions, it will reflect the ethical framing embedded in its alignment dataset.
3. Safety & Guardrails: Inherits safety policies from Meta's Llama 3.2 community license and base alignment.

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📖 Citation & Acknowledgements

If you utilize this model or adapter in your research, applications, or benchmarks, please cite:

@misc{hipparagi2026llama32wisdom,
  author = {Sandeep Hipparagi},
  title = {Llama-3.2-3B-Instruct: LoRA Adapter for Philosophical Reasoning and Strategic Wisdom},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/Sandeep4235/llama3-2-lora-wisdom}},
}

Acknowledgements

• Meta AI for the Llama 3.2 architecture.
• Unsloth AI for ultra-fast, memory-efficient QLoRA kernels.