license, base_model, library_name, pipeline_tag, language

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

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:

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

Citations

If you use this model/dataset, please cite:

@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}, 
}