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Model: qbz506/nyaya-llama-3b-stage0-full
Source: Original Platform

README.md

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+---
+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}, 
+}
+