cyborgxx101/ gemma4-vedic-opus-finetuned-v2:q8_0

46 Downloads Updated 1 month ago

A fine-tuned version of Gemma 4 12B IT on Vedic wisdom literature blended with general reasoning data.

tools thinking

ollama run cyborgxx101/gemma4-vedic-opus-finetuned-v2:q8_0

curl http://localhost:11434/api/chat \
  -d '{
    "model": "cyborgxx101/gemma4-vedic-opus-finetuned-v2:q8_0",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

from ollama import chat

response = chat(
    model='cyborgxx101/gemma4-vedic-opus-finetuned-v2:q8_0',
    messages=[{'role': 'user', 'content': 'Hello!'}],
)
print(response.message.content)

import ollama from 'ollama'

const response = await ollama.chat({
  model: 'cyborgxx101/gemma4-vedic-opus-finetuned-v2:q8_0',
  messages: [{role: 'user', content: 'Hello!'}],
})
console.log(response.message.content)

Details

Updated 1 month ago

1 month ago

7b9364c29f81 · 13GB ·

model

archgemma4

·

parameters11.9B

·

quantizationQ8_0

13GB

template

{{ if .System }}<|turn>system {{ .System }}<turn|> {{ end }}{{ range $i, $_ := .Messages }}{{ if eq

249B

license

apache-2.0

10B

params

{ "stop": [ "<turn|>" ], "temperature": 0.2, "top_p": 0.95 }

62B

Readme

Gemma 4 12B — Vedic Opus Finetuned v2

A scholarly-grade fine-tune of Gemma 4 12B (text-only) for rigorous Vedic studies. Built to answer questions about the Ṛgveda, Brāhmaṇas, Upaniṣads, Vedāṅgas, Pūrva Mīmāṃsā, and Vedānta with philological precision.

Key Features

Chronological awareness — training examples tagged with [Epoch: Saṃhitā], [Epoch: Upaniṣad], etc. so the model distinguishes Vedic layers
Contrastive pairs — explicit cross-epoch comparisons
Anachronism detection — trained to say “this concept does not appear in that text” when appropriate
Mīmāṃsā hermeneutics — proper handling of śāstra-vihita-hiṃsā, apūrva, vidhi/niṣedha
Scholarly system prompt — mandates citation of specific texts, epochs, and technical terms
Zero New Age content — purely philological, no devotional filler

What the Model Can Do

Define technical terms with chronological precision
Distinguish Ṛta (cosmic order in the Saṃhitā) from Brahman (Vedāntic absolute)
Explain Mīmāṃsā hermeneutics (vidhi, apūrva, arthāpatti, śāstra-vihita-hiṃsā)
Trace semantic evolution (e.g., brahman from “sacred utterance” to “ultimate reality”)
Detect anachronistic projections of later concepts onto earlier texts

HumanEval Benchmark

Accuracy: 91.5% (¹⁵⁰⁄₁₆₄) — competitive with SOTA 12B models; Vedic fine-tuning did not degrade general coding/reasoning.

Recommended Settings

temperature: 0.2, top_p: 0.95, repetition_penalty: 1.15

Quantizations

Q4_K_M (~7 GB) — :latest on Ollama
Q8_0 (~12 GB) — :q8_0 on Ollama
F16 (~23 GB) — GGUF repo

# Gemma 4 12B — Vedic Opus Finetuned v2

A scholarly-grade fine-tune of Gemma 4 12B (text-only) for rigorous Vedic studies. Built to answer questions about the Ṛgveda, Brāhmaṇas, Upaniṣads, Vedāṅgas, Pūrva Mīmāṃsā, and Vedānta with philological precision.

## Key Features
- **Chronological awareness** — training examples tagged with `[Epoch: Saṃhitā]`, `[Epoch: Upaniṣad]`, etc. so the model distinguishes Vedic layers
- **Contrastive pairs** — explicit cross-epoch comparisons
- **Anachronism detection** — trained to say "this concept does not appear in that text" when appropriate
- **Mīmāṃsā hermeneutics** — proper handling of śāstra-vihita-hiṃsā, apūrva, vidhi/niṣedha
- **Scholarly system prompt** — mandates citation of specific texts, epochs, and technical terms
- **Zero New Age content** — purely philological, no devotional filler

## What the Model Can Do
- Define technical terms with chronological precision
- Distinguish Ṛta (cosmic order in the Saṃhitā) from Brahman (Vedāntic absolute)
- Explain Mīmāṃsā hermeneutics (vidhi, apūrva, arthāpatti, śāstra-vihita-hiṃsā)
- Trace semantic evolution (e.g., brahman from "sacred utterance" to "ultimate reality")
- Detect anachronistic projections of later concepts onto earlier texts

## HumanEval Benchmark
Accuracy: **91.5%** (150/164) — competitive with SOTA 12B models; Vedic fine-tuning did not degrade general coding/reasoning.

## Recommended Settings
temperature: 0.2, top_p: 0.95, repetition_penalty: 1.15

## Quantizations
- Q4_K_M (~7 GB) — `:latest` on Ollama
- Q8_0 (~12 GB) — `:q8_0` on Ollama
- F16 (~23 GB) — GGUF repo

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