---
license: llama3.2
base_model: unsloth/llama-3.2-3b-instruct
library_name: transformers
pipeline_tag: text-generation
language:
  - en
---

# Pramana Stage 0 (Full Merged Weights)

This folder contains **full merged weights** for the Stage 0 Nyaya-structured model. It does **not** require a LoRA adapter at inference time.

## What is included

- `model-00001-of-00002.safetensors`
- `model-00002-of-00002.safetensors`
- `model.safetensors.index.json`
- `config.json`, `tokenizer.json`, `tokenizer_config.json`, `chat_template.jinja`
- `nyaya-llama-3b-stage0-merged-q4.gguf` (quantized full model for Ollama)

## Base model

- `unsloth/llama-3.2-3b-instruct`

## Usage (Transformers)

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "qbz506/nyaya-llama-3b-stage0"
subfolder = "full/nyaya-llama-3b-stage0-merged"

model = AutoModelForCausalLM.from_pretrained(
    repo_id,
    subfolder=subfolder,
    torch_dtype="auto",
    device_map="auto",
)

tokenizer = AutoTokenizer.from_pretrained(
    repo_id,
    subfolder=subfolder,
    use_fast=True,
)
```

## Usage (Ollama with GGUF)

Download `nyaya-llama-3b-stage0-merged-q4.gguf`, then:

```bash
cat > Modelfile <<EOM
FROM ./nyaya-llama-3b-stage0-merged-q4.gguf
SYSTEM """
You are a Nyaya reasoning engine. Follow the exact output format provided.
"""
PARAMETER temperature 0
PARAMETER top_p 1
EOM

ollama create nyaya-llama-3b-stage0-merged-q4 -f Modelfile
ollama run nyaya-llama-3b-stage0-merged-q4 "<your prompt>"
```

## Prompting

Use the exact Nyaya section headers for best adherence:

```
## Samshaya (Doubt Analysis)
## Pramana (Sources of Knowledge)
## Pancha Avayava (5-Member Syllogism)
## Tarka (Counterfactual Reasoning)
## Hetvabhasa (Fallacy Check)
## Nirnaya (Ascertainment)
```

## Intended use

This model is tuned for structured 6-phase Nyaya reasoning on logic-style problems. It is research-grade and optimized for format adherence over open-ended creativity.

## Limitations

- Responses may be verbose due to strict format.
- Best results require the exact section headers and a system prompt.
- Not evaluated for safety-critical domains.
Paper: [Pramana: Fine-Tuning Large Language Models for Epistemic Reasoning through Navya-Nyaya](https://arxiv.org/abs/2604.04937)
## Citations


If you use this model/dataset, please cite:

```bibtex
@misc{sathish2026pramanafinetuninglargelanguage,
      title={Pramana: Fine-Tuning Large Language Models for Epistemic Reasoning through Navya-Nyaya}, 
      author={Sharath Sathish},
      year={2026},
      eprint={2604.04937},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2604.04937}, 
}