---

license: llama3

license_name: llama-3-community-license

license_link: https://llama.meta.com/llama3/license/

base_model: unsloth/llama-3-8b-bnb-4bit

tags:

• text-generation
• pytorch
• safetensors
• trl
• transformers
• unsloth
• lora
• sft
• peft
• llama-3
• gguf
• wisdom
• spiritual-guidance
• instruction-following
• philosophical-inquiry

model_creator: BluePatterns AI

model_type: causal-lm

language:

• en

pipeline_tag: text-generation

library_name: transformers

widget:

• text: "### Instruction: How can one maintain internal clarity amidst daily stress?\n\n### [Internal Thought]: Analyzing intent.\n\n### [Author Response]:"

example_title: Inner Clarity

• text: "### Instruction: What is the significance of self-awareness in daily life?\n\n### [Internal Thought]: Analyzing intent.\n\n### [Author Response]:"

example_title: Self-Awareness

---

llama3-8b-Wisdom

Llama-3 8B · LoRA Fine-tuned · Spiritual & Philosophical Guidance · GGUF Quantized

---

🚀 Model Summary

llama3-8b-Wisdom is an 8-billion parameter causal language model fine-tuned (via Unsloth QLoRA) from unsloth/llama-3-8b-bnb-4bit to specialize in spiritual guidance, philosophical inquiry, and contemplative reflection — delivering concise, context-specific responses rooted in ancient spiritual, mystic, and yogic traditions. Unlike general-purpose chat models, Wisdom is trained to follow a strict three-stage prompt format (Instruction → Internal Thought → Author Response) that produces grounded, reflective answers without the excessive verbosity and conversational loops typical of base instruct models.

Mission. BluePatterns AI built Wisdom to bridge deep existential insights with modern conceptual understanding — making contemplative wisdom from ancient traditions accessible through AI, while maintaining a clear boundary between reflective guidance and professional advice.

---

💡 Model Architecture & Specifications

Wisdom inherits the dense Transformer architecture of Llama-3 8B and applies a LoRA adapter trained via Unsloth's 4-bit QLoRA pipeline on a custom contemplative-text dataset.

| Specification | Detail |

|---|---|

| Developed by | Sandeep Hipparagi (BluePatterns AI) |

| Model type | Causal Language Model (Causal-LM) |

| Base model | unsloth/llama-3-8b-bnb-4bit (Llama-3 8B, 4-bit NF4) |

| Total parameters | 8.03B |

| Architecture | Dense Transformer, decoder-only (auto-regressive) |

| Attention | Grouped-Query Attention (GQA) — 32 Q heads, 8 KV heads |

| Layers | 32 |

| Hidden size | 4,096 |

| FFN dimension | 14,336 |

| Head dimension | 128 |

| Positional encoding | RoPE (theta = 500,000) |

| Normalization | RMSNorm (pre-normalization) |

| Activation | SwiGLU |

| Vocabulary size | 128,256 |

| Native context length | 8,192 tokens |

| Tokenizer | BPE (tiktoken-based, 128K vocabulary) |

| Fine-tuning method | Unsloth QLoRA (4-bit NF4 quantization) |

| LoRA rank (r) | 32 |

| LoRA alpha | 64 |

| LoRA dropout | 0 |

| Adapter targeting | q_proj, k_proj, v_proj, o_proj |

| Quantization (this repo) | GGUF Q4_K_M |

| Training data | Custom contemplative-text dataset (cleaned & chunked) |

| Optimizer | AdamW 8-bit |

| Learning rate | 2e-4, linear schedule |

| Epochs / Steps | 251 steps |

| Batch size (per device) | 2 |

| Gradient accumulation steps | 4 |

| Warmup steps | 10 |

| Weight decay | 0.01 |

| Seed | 3407 |

| Max sequence length (training) | 2,048 tokens |

| Training precision | bf16 mixed precision |

| Language(s) | English |

| License | Llama 3 Community License |

---

🛠 Quickstart & Usage

Prompt Format

Wisdom uses a strict three-stage prompt template. Always format your input as follows:

### Instruction: {Your Question or Instruction}

### [Internal Thought]: Analyzing intent.

### [Author Response]:

Option A — Python with transformers (recommended for GPU)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Sandeep4235/llama3-8b-Wisdom-gguf"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.bfloat16,
)

prompt = (
    "### Instruction: How can one maintain internal clarity "
    "amidst daily stress?\n\n"
    "### [Internal Thought]: Analyzing intent.\n\n"
    "### [Author Response]:"
)

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    output_ids = model.generate(
        **inputs,
        max_new_tokens=256,
        do_sample=True,
        temperature=0.85,
        top_p=0.95,
        repetition_penalty=1.3,
        pad_token_id=tokenizer.eos_token_id,
    )

generated = output_ids[0][inputs["input_ids"].shape[-1]:]
response = tokenizer.decode(generated, skip_special_tokens=True)
print(response)

Option B — llama.cpp CLI (CPU / edge inference)

./llama-cli \
  -m llama-3-8b-Wisdom-Q4_K_M.gguf \
  -p '### Instruction: What is the significance of self-awareness in daily life?

### [Internal Thought]: Analyzing intent.

### [Author Response]:' \
  -n 256 \
  --temp 0.85 \
  --top-p 0.95 \
  --repeat-penalty 1.3

Option C — Python with llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="Sandeep4235/llama3-8b-Wisdom-gguf",
    filename="llama-3-8b-Wisdom-Q4_K_M.gguf",
    n_ctx=4096,
    n_gpu_layers=-1,
)

prompt = (
    "### Instruction: How can one maintain internal clarity "
    "amidst daily stress?\n\n"
    "### [Internal Thought]: Analyzing intent.\n\n"
    "### [Author Response]:"
)

output = llm(
    prompt,
    max_tokens=256,
    temperature=0.85,
    top_p=0.95,
    repeat_penalty=1.3,
    stop=["### Instruction:"],
)

print(output["choices"][0]["text"])

---

📊 Evaluation and Benchmarks

Formal quantitative benchmarks for Wisdom (e.g., perplexity, ROUGE, or human-preference scores against baseline models) have not yet been published. Performance was assessed through iterative qualitative evaluation against a held-out validation set, focusing on output format adherence, conciseness, and relevance.

Qualitative Evaluation Summary

| Evaluation Criterion | Method | Outcome |

|---|---|---|

| Format adherence | Manual review of 50+ generations | Improved conformance to the Author Response schema; reduced format violations |

| Verbosity control | Token-length comparison vs. base Llama-3 8B Instruct | Significantly reduced excessive output length |

| Conversational loop prevention | Extended multi-turn testing | Eliminated self-generation of questions and repetitive loops |

| Contextual relevance | Domain-expert spot-checking on spiritual/yogic topics | Responses are context-specific and grounded in contemplative traditions |

General Academic Benchmarks (inherited from base)

The following scores reflect the Llama-3 8B base model as published by Meta and are provided for reference only — they are not measurements of Wisdom itself.

| Benchmark | Llama-3 8B (base) | Metric |

|---|---|---|

| MMLU (5-shot) | 66.6 | Pass@1 accuracy |

| HumanEval (0-shot) | 60.9 | Pass@1 |

| GSM8K (8-shot) | 79.6 | Pass@1 accuracy |

> These figures are the base model's published results and do not represent Wisdom's fine-tuned performance on spiritual or philosophical tasks. Independent benchmarking is encouraged.

---

⚠️ Intended Uses & Limitations

Primary Use Cases

• Philosophical exploration — generating reflective perspectives on existential questions, ethical dilemmas, and the nature of self.
• Spiritual guidance — providing context-aware responses drawn from yogic science, contemplative traditions, and inner-inquiry frameworks.
• Reflective conversational AI — serving as a contemplative companion for mindfulness, meditation, and self-awareness practices.
• Educational content — assisting in the creation of teaching materials for philosophy, comparative spirituality, and contemplative studies.

Out-of-Scope Uses

• Medical, psychiatric, or therapeutic advice. Wisdom is not a substitute for professional mental health care, crisis intervention, or medical treatment.
• Legal or financial guidance. The model should not be relied upon for legal counsel, financial decisions, or regulatory compliance.
• Factual authority. Do not use the model as a definitive source for historical dates, scriptural citations, or scientific facts without external verification.
• High-stakes automated decision-making. The model should not be deployed in systems where outputs directly affect individual rights, safety, or welfare without human oversight.
• Harmful or discriminatory content generation. The model must not be used to generate content that promotes discrimination, exploitation, or harm against any individual or group.

Limitations & Biases

• Interpretive scope. The model's responses are derived from its training on philosophical and contemplative literature. Interpretations are inherently limited by this data and may not encompass the full breadth of human thought, cultural diversity, or scientific understanding.
• Hallucination risk. Like all LLMs, Wisdom may generate plausible but incorrect or fabricated information. Always verify scriptural citations, historical claims, and technical terminology against authoritative sources.
• Cultural bias. The training data may over-represent certain spiritual traditions (e.g., yogic and mystic frameworks) relative to others. Responses may reflect this distribution.
• English-only. Training data is English-centric; quality in other languages is untested and likely degraded.
• Training scale. The model was fine-tuned for 251 steps on a modest custom dataset. It is not intended to match the breadth of large-scale instruction-tuned models.
• Quantization loss. The Q4_K_M GGUF quantization introduces minor quality degradation compared to the full-precision adapter. Use Q8_0 for near-lossless quality.

---

🔒 Responsible AI & Safety Alignment

Alignment Techniques

• Supervised Fine-Tuning (SFT). The model was trained on curated instruction pairs that demonstrate reflective, safe, and context-appropriate responses. No RLHF or DPO was applied in this release.
• Structured prompt format. The three-stage template (Instruction → Internal Thought → Author Response) constrains the model to produce grounded, deliberate outputs rather than free-form generation.
• Inference-time controls. Recommended sampling parameters (temperature=0.85, repetition_penalty=1.3, top_p=0.95) are tuned to reduce repetition and verbosity while preserving thoughtful, varied responses.

Deployment Recommendations

1. Input validation. Sanitize all user inputs before passing them to the model. Reject inputs that attempt to bypass the prompt template or inject malicious instructions.
2. Output audit. Review generated responses for factual accuracy and appropriateness before use in any public-facing context.
3. Content filtering. Pair Wisdom with an input/output content filter (e.g., Llama Guard, NeMo Guardrails) for production deployments.
4. Rate limiting. Deploy behind an API gateway with authentication and rate limiting to prevent abuse.
5. Guardrail system prompt. Prepend a safety directive such as:

```

You are a reflective guide versed in contemplative traditions.

Refuse requests for medical, psychiatric, legal, or financial advice.

If a user appears to be in crisis, respond with empathy and

direct them to appropriate professional support resources.

```

1. Crisis protocol. If deploying Wisdom in a user-facing application, implement a crisis-detection layer that identifies distress signals and routes users to local mental health crisis resources.

---

📦 Quantization Variants

This repository provides GGUF quantizations for CPU and GPU inference via llama.cpp and compatible runtimes. The default and recommended variant is Q4_K_M.

| File | Quantization | Approx. Size | Use Case |

|---|---|---|---|

| llama-3-8b-Wisdom-Q4_K_M.gguf | Q4_K_M | ~4.9 GB | Recommended — best balance |

| llama-3-8b-Wisdom-Q5_K_M.gguf | Q5_K_M | ~5.7 GB | Higher quality, more VRAM |

| llama-3-8b-Wisdom-Q8_0.gguf | Q8_0 | ~8.5 GB | Near-lossless |

> Verify which quantization files are present in the repository file listing above.

---

🏋️ Training Details

Training Data

The fine-tuning dataset was constructed from contemplative and philosophical source texts using a multi-stage preprocessing pipeline:

1. Ligature error correction. Extraction artifacts (<e, <is, <ere) were identified and corrected to restore original word forms.
2. Whitespace standardization. Irregular spacing, tab characters, and non-breaking spaces were normalized to single spaces.
3. Sliding-window chunking. Text was segmented into ~500-word samples with a 100-word overlap, formatted into the three-stage instruction template. Chunks shorter than 150 words were discarded.
4. Train/validation split. The resulting dataset was split into a training set and a held-out validation set for qualitative evaluation.

Training Environment

| Parameter | Value |

|---|---|

| Hardware | Tesla T4 GPU (16 GB VRAM) |

| Cloud provider | Google Colab |

| Training duration | ~4 hours |

| Fine-tuning framework | Unsloth |

| Deep learning framework | PyTorch |

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

| Metric | Value |

|---|---|

| Hardware type | Tesla T4 GPU |

| Hours used | ~4 hours |

| Cloud provider | Google Colab |

| Estimated CO₂eq | Low (single GPU, short training run) |

---

🤝 Citation & Acknowledgements

If you use llama3-8b-Wisdom in your research or product, please cite it as follows:

@misc{hipparagi2025wisdom,
  title        = {llama3-8b-Wisdom: A LoRA Fine-tuned Llama-3 8B Model for Spiritual and Philosophical Guidance},
  author       = {Sandeep Hipparagi},
  organization = {BluePatterns AI},
  year         = {2025},
  url          = {https://huggingface.co/Sandeep4235/llama3-8b-Wisdom-gguf},
  note         = {Fine-tuned from unsloth/llama-3-8b-bnb-4bit under the Llama 3 Community License}
}

Acknowledgements

• Meta AI — for open-sourcing the Llama-3 8B model family under the Llama 3 Community License.
• Unsloth — for the efficient QLoRA fine-tuning framework that made this release possible.
• Hugging Face — for model hosting and the open ML ecosystem.

---

📬 Contact

Sandeep Hipparagi — AI Developer, Co-Founder of BluePatterns AI

• Hugging Face: Sandeep4235
• Organization: BluePatterns AI

BluePatterns AI is focused on bridging the gap between frontier and open-source models — democratizing access to reliable, accessible intelligence for diverse communities.

---

Built by Sandeep Hipparagi · BluePatterns AI · Llama 3 Community License