Main machine: Z890-AI Top + dual RTX 5090 = 32 GB + 32 GB = 64 GB VRAM, 256 GB RAM
Second machine: Z490 + RTX 3090 24 GB VRAM, 128 GB RAM
Laptop: Omen17 + RTX 3070 Ti 8 GB VRAM, 64 GB RAM
Large local model available: public Grok-2 copy on HDD, approx. 298 GB

The goal is not to chase fashionable AI claims, but to build useful local systems for Sanskrit corpus work, Jyotiṣa rule modelling, MBFR documents, pure-Hindi generation, code assistance, video/text pipelines, and tabular forecasting such as gold, rainfall, weather, and related numeric models.

1. Corrected Starting Point

The older notes contained useful ideas but also stale or over-optimistic claims. The corrected position is this:

Grok-2 is locally present as a 298 GB public copy, but it should not be assumed to be easily trainable or fast on dual RTX 5090.
It should be treated as a large experimental local model for inference, batch reasoning, comparison, and perhaps adapter experiments only after successful serving tests.

The official xAI/Hugging Face instructions for their Grok-2 repository describe a much larger expected folder and a server launch using tensor parallelism across 8 GPUs, each with more than 40 GB memory. Therefore, my 298 GB copy is likely a different packaging, quantization, or checkpoint layout. It may still be useful, but it must be tested empirically.

1.1 What Must Not Be Assumed

Do not assume:

that Grok-2 will load directly into 64 GB VRAM
that it will run fast from HDD
that QLoRA/PEFT training on Grok-2 will be straightforward
that Grok-2 is the best model for deterministic rule systems
that a 298 GB model automatically gives better results than a well-tuned 30B–34B model for my own rule-based domains

1.2 What Can Be Assumed

The following assumptions are reasonable:

Z890 dual RTX 5090 is excellent for 30B–34B local model work
RTX 3090 is excellent for 24B–34B quantized inference, dataset preparation, embeddings, and smaller training jobs
Omen17 RTX 3070 Ti is useful for light inference, preprocessing, coding, testing, and data cleaning
Grok-2 can be investigated as a large local inference model, especially if available in a format usable by llama.cpp, SGLang, vLLM, or another backend
HDD should be treated as archive storage, not ideal runtime storage for large model inference

2. Hardware Roles

2.1 Z890-AI Top + Dual RTX 5090 + 256 GB RAM

Primary role: serious model experimentation, 30B–34B fine-tuning, QLoRA, RAG indexing, document pipelines, batch inference, and high-throughput ML.

Best uses:

full or near-full fine-tuning experiments on 7B–14B models
QLoRA/LoRA fine-tuning on 30B–34B models
multi-GPU inference for 30B–70B quantized models
Sanskrit/Purāṇa corpus embedding and retrieval
MBFR document assistant
pure-Hindi style model/adapters
code-assistance models for VB6, C++, Python, HTML, Wikidot, and batch scripts
tabular ML training for gold/rainfall/weather using MLP, XGBoost, LightGBM, etc.
video pipeline assistance and script generation

This is the machine where most serious AI work should happen.

2.2 Z490 + RTX 3090 + 128 GB RAM

Secondary role: inference server, embeddings server, smaller LoRA work, RAG backend, validation machine, and batch preprocessing.

Best uses:

serve a 14B–32B quantized model
run embedding models continuously
build vector databases
test fine-tuned adapters before deployment
process Sanskrit text, CSVs, and corpus chunks
run DeOldify/Real-ESRGAN style pipelines where RTX 3090 is already stable
compare outputs against the Z890 model

The RTX 3090 remains very valuable because of its 24 GB VRAM and mature CUDA compatibility.

2.3 Omen17 + RTX 3070 Ti 8 GB + 64 GB RAM

Portable role: testing, lightweight inference, script development, dataset inspection, small models, and remote access to the main machines.

Best uses:

run 3B–8B quantized models
test prompts and dataset formatting
prepare CSV/JSONL training samples
run small embedding jobs
remote desktop / SSH / web UI access to Z890 and Z490
light coding assistant
emergency inference when away from main machines

The Omen17 should not be forced to run huge models locally. Its value is mobility and control.

3. Model Strategy: Do Not Use One Model for Everything

The correct architecture is multi-model, not one giant model doing all tasks.

Wrong strategy: “Use Grok-2 for everything: Jyotiṣa, Sanskrit grammar, gold prediction, weather prediction, MBFR, code, pure Hindi, and corpus retrieval.”

Correct strategy: Use different model types for different tasks:

LLM for language and reasoning
RAG for document-grounded answers
MLP/tabular models for numeric prediction
rule engine for deterministic logic
small router to decide which subsystem should answer

3.1 Recommended Role Division

Task	Best System
Pure conversation, explanation, essay drafting	24B–34B instruct LLM; optionally Grok-2 if it runs acceptably
Sanskrit/Purāṇa retrieval	RAG + embeddings + reranker + LLM explanation
Jyotiṣa deterministic rule engine	VB6/C++/Python rule engine + structured output, not free-form LLM
PhalitaGPT explanation layer	Rule engine first, LLM second
Gold/weather/rainfall numeric prediction	MLP / tabular ML, not LLM
Pure Hindi generation	LLM with style dataset + lexical filter
MBFR Q&A	RAG over my MBFR documents + LLM synthesis
Code assistant	Qwen Coder / DeepSeek Coder / similar local model, plus general LLM
Grok-2	experimental large-model inference, batch reasoning, comparison, and possibly style/adapters if feasible

4. Grok-2: Mostly Frozen Adapter Use

Correct use of my local Grok-2 copy:
I am not planning to fully fine-tune Grok-2. I am planning to keep the Grok-2 backbone mostly frozen and train only a small project-specific adapter using PEFT/LoRA/QLoRA-style methods, if the model format and loading backend allow it.

My local Grok-2 copy is approximately 298 GB and is publicly accessible. It should be treated as a valuable large-model base for one carefully chosen adapter experiment, not as a model whose main weights should be altered.

4.1 What “Mostly Frozen Grok-2” Means

In this approach:

the original Grok-2 weights remain unchanged
only a small number of adapter parameters are trained
the trained output is saved separately as a small adapter
the base Grok-2 model can be reused unchanged
the adapter can be loaded only when that project is needed

This is not full fine-tuning. It is a controlled method for adding one project-specific behaviour without corrupting the main model.

4.2 Why This Is Useful

A frozen Grok-2 backbone can retain its general reasoning, language, and broad knowledge, while the adapter teaches it one specific discipline or response-pattern.

Possible adapter projects:

Adapter Project	Purpose
Pure-Hindi style adapter	generate Hindi avoiding Urdu/Persian/Arabic vocabulary where required
MBFR explanation adapter	explain MBFR concepts using my terminology and structure
Jyotiṣa explanation adapter	explain already-computed rule-engine outputs without inventing calculations
Sanskrit corpus assistant adapter	improve response style for Sanskrit/Purāṇa passages retrieved by RAG
Wikidot writer adapter	produce clean Wikidot edit-mode pages with headings, colour blocks, and formatting

The best first project should be small, sharply defined, and easy to evaluate.

4.3 Best First Grok-2 Adapter Project

The first Grok-2 adapter should not be gold prediction, rainfall prediction, or any numeric forecasting model. Those belong to MLP/tabular models.

The best first Grok-2 adapter should be one of these:

Pure-Hindi style adapter
MBFR explanation adapter
Wikidot formatting adapter
Jyotiṣa explanation-only adapter

Among these, the safest first experiment is:

Recommended first Grok-2 adapter: Pure-Hindi + Wikidot writing adapter.
Reason: output quality is easy to judge, the task is linguistic rather than numerically deterministic, and failure will not corrupt any scientific or forecasting workflow.

4.4 Practical Training Method

The training should be done as follows:

keep Grok-2 base weights frozen
load the model with the lightest working backend
train only LoRA/PEFT adapter layers
use a small curated dataset first
compare base Grok-2 output vs adapter output
save only adapter weights
do not merge adapter into the base model until extensive testing is complete

4.5 Suggested Adapter Settings

Initial conservative settings:

Parameter	First Test Value
LoRA rank	4 or 8
LoRA alpha	16 or 32
dropout	0.05
target modules	attention projection layers first, exact names discovered from model structure
batch size	smallest stable value
gradient accumulation	increase if needed
sequence length	begin small, then increase after stability
dataset size for smoke test	100–500 examples
dataset size for first real test	2,000–10,000 examples

Do not begin with a huge dataset. First prove that the adapter changes behaviour in the intended direction.

4.6 Dataset Format

For a pure-Hindi/Wikidot adapter, use examples like:

{
  "instruction": "Rewrite the following material in clean Wikidot edit mode using pure Hindi where appropriate, preserving technical English terms.",
  "input": "Raw notes or rough markdown text here.",
  "output": "+ Main Heading\n\n[[div style=\"background:#f0fdf4;border-left:6px solid #16a34a;padding:12px 16px;margin:12px 0;border-radius:8px;\"]]\n**Corrected polished text here.**\n[[/div]]"
}

For MBFR:

{
  "instruction": "Explain this MBFR section in a clear technical style without exaggeration.",
  "input": "Retrieved MBFR notes, assumptions, equations, or draft section.",
  "output": "Structured explanation with assumptions, limits, and conclusion."
}

For Jyotiṣa explanation:

{
  "instruction": "Explain this computed Jyotiṣa result. Do not invent chart data.",
  "input": {
    "computed_factors": "...",
    "rules_applied": "...",
    "conflicting_factors": "..."
  },
  "output": "Explanation based only on the supplied factors."
}

4.7 Important Boundary

Grok-2 adapter training should be used for language, explanation, structure, and reasoning style.

It should not replace:

my VB6/C++ deterministic Jyotiṣa calculations
my rule-engine logic
my MLP models for gold/weather/rainfall
my RAG database for Sanskrit, Purāṇas, MBFR, or research documents

Correct relation:

Exact computation / retrieval / prediction
        ↓
Structured facts
        ↓
Frozen Grok-2 + small adapter
        ↓
Clear explanation / pure Hindi / Wikidot / synthesis

4.8 Hardware Use

The Z890 dual RTX 5090 machine should be used for this Grok-2 adapter experiment.

The Z490 RTX 3090 machine should support:

dataset preparation
RAG indexing
embeddings
evaluation
comparison with smaller models

The Omen17 should be used for:

editing datasets
running small tests
remote control
reviewing outputs

4.9 Final Rule for Grok-2

Grok-2 should be used as a mostly frozen large reasoning and language base.
Only one small, sharply defined adapter should be trained first.
The adapter should improve style, structure, and domain explanation — not replace deterministic computation or numeric ML models.

5. Serious Rule-Based Work: Use 30B–34B Local Models, Not Grok-2 First

For my serious deterministic domains, a smaller but controllable model is better than a huge model that cannot be controlled.

5.1 Suitable Domains

Use 30B–34B local models for:

PhalitaGPT explanation layer
Sanskrit grammar assistant
pure-Hindi generation
MBFR document assistant
Vedic corpus summarization
Jyotiṣa rule explanation
code generation for VB6/C++/Python
structured response generation from rule-engine outputs

5.2 Why 30B–34B Is the Practical Sweet Spot

30B–34B models are large enough to reason well, but small enough to:

load on dual RTX 5090 in quantized form
fine-tune using QLoRA
run on RTX 3090 in smaller quantizations
produce acceptable speed
allow repeated experiments
avoid the enormous overhead of Grok-2

5.3 Candidate Model Families

The exact model should be chosen after testing, but the practical candidates are:

Model Family	Use
Qwen 3 / Qwen 2.5 32B	strong multilingual, reasoning, code, and general use
Yi 34B / Yi 1.5 34B	strong open-weight 34B option
Mistral Small 24B	efficient, strong general assistant, long context variants
Qwen Coder 32B	code generation, script writing, conversion, debugging
smaller 7B–14B models	fast router, classifier, assistant for laptop

5.4 Full Fine-Tuning vs QLoRA

Important distinction:
For 30B–34B models, true full fine-tuning is expensive and risky. QLoRA/LoRA is usually the first practical method. Full fine-tuning should be attempted only for smaller models or after the dataset is proven.

Method	Practical Meaning	Use
Full fine-tuning	update all model weights	only for smaller models or final serious experiments
LoRA	train adapter matrices, base model unchanged	best first step
QLoRA	quantized base model + LoRA adapters	best practical method for 30B–34B
Continued pretraining	train on raw corpus text	useful for Sanskrit/Hindi corpus adaptation but must be done carefully
SFT	instruction-response training	best for assistant behavior
DPO/ORPO/KTO	preference tuning	later stage, after good SFT

For my work, the order should be:

Build clean dataset
Train small model first
Test outputs
Train 30B–34B QLoRA
Compare with rule engine
Add RAG
Only then consider deeper/full tuning

6. PhalitaGPT and Jyotiṣa: Correct Architecture

6.1 Do Not Let an LLM Invent Predictions

For Jyotiṣa, the LLM must not be the primary predictor.
The primary predictor should be my deterministic rule engine, classical rule database, computed chart features, varga features, dasha/gochara features, and tested scoring models.

Correct architecture:

VB6/C++ astrology engine computes chart and varga features
Rule engine applies classical rules
ML/regression layer estimates weights where needed
LLM explains the result in human language
RAG supplies textual support from classical corpus
Final output cites rule sources and computed factors

6.2 PhalitaGPT Layer Separation

Layer	Responsibility
Calculation engine	exact astronomy/chart/varga/dasha calculations
Rule engine	deterministic application of Jyotiṣa rules
Statistical layer	weight estimation from large horoscope database
RAG layer	retrieve classical verses/commentaries
LLM layer	explanation, synthesis, report writing
Human review	final judgment for serious cases

6.3 Output Discipline

PhalitaGPT should output:

computed facts
applied rules
rule weights
conflicting indications
final synthesis
confidence level
uncertainty causes
references to retrieved corpus passages

It should not output vague soothing language.

7. Sanskrit Grammar and Pure Hindi

7.1 Sanskrit Grammar

Sanskrit grammar is symbolic, layered, and rule-bound. Therefore:

use LLM for explanation
use rule engine for derivation
use corpus lookup for examples
use constrained output formats where possible
avoid allowing the base model to “guess” grammatical derivations

Recommended modules:

Module	Function
Sandhi engine	deterministic phonetic transformations
Subanta/tiṅanta analyzer
Dhātu database
Sūtra retriever
Example retriever
LLM explainer

7.2 Pure Hindi Generation

Pure Hindi generation is not merely “translate into Hindi.” It needs:

Sanskritic vocabulary preference
removal/avoidance of Urdu/Persian/Arabic loanwords where desired
controlled register: not too difficult, not vulgarized
technical English retained where useful
custom lexical filter
training samples from accepted style

Best method: train a style adapter or SFT dataset for pure Hindi, then add a post-generation lexical checker.
Do not rely only on prompting.

Pipeline:

generate draft
scan for banned/undesired loanwords
replace with preferred Sanskritic alternatives
check readability
optionally regenerate sentence by sentence
final human review

8. RAG for Sanskrit, Purāṇas, MBFR, and Research

8.1 Why RAG Is Essential

Fine-tuning should not be used as a dumping ground for all knowledge. A model should not be expected to memorize entire Purāṇas, MBFR pages, commentaries, and research notes.

Correct approach:

use RAG for factual recall
use LLM for reasoning and synthesis
use fine-tuning for style, behavior, and task discipline

8.2 Sanskrit/Purāṇa RAG

Corpus pipeline:

collect Devanagari texts
normalize encoding
remove page noise, headers, footnotes where needed
preserve references such as chapter/verse markers
split by verse, passage, or semantic unit
create metadata: text, section, source, topic, deity, speaker, meter if available
embed passages
add keyword index
add reranker
generate answer only from retrieved passages

8.3 MBFR RAG

For MBFR:

keep HTML/Markdown/Wikidot sources clean
chunk by heading and subheading
preserve equations and assumptions
index design scenarios separately
include safety/governance passages
retrieve relevant technical and policy sections together

The LLM should answer:

what the design claims
what assumptions are used
what calculations support it
what risks remain
what governance safeguards are required

9. Numeric Prediction: Gold, Rainfall, Weather, etc.

9.1 LLM Is Not the Predictor

For gold, rainfall, weather, earthquake indicators, and similar numeric outputs, the main model should be MLP or another tabular/time-series model, not Grok-2 and not a general LLM.

The old house-price analogy is correct:

many input parameters
one or more numeric outputs
output indicates magnitude, probability, or strength

Examples:

Domain	Inputs	Output
Gold price	macroeconomic, market, astrological, time-cycle, historical data	price change / strength score
Rainfall	date, location, season, pressure, humidity, chart factors, historical rainfall	rainfall magnitude
Weather severity	observed + computed features	severity score
Earthquake risk research	geological + temporal + celestial indicators where tested	probability/strength score

9.2 Recommended ML Models

Use these in order:

baseline linear/logistic regression
Random Forest / Gradient Boosting
XGBoost / LightGBM / CatBoost
MLP
LSTM/Transformer only if sequence structure justifies it

Do not start with a giant model. Start with measurable baselines.

9.3 Correct LLM Role in Forecasting

The LLM can:

explain the numeric output
generate reports
compare today with prior similar cases
retrieve historical examples
write code
summarize feature importance
produce pure-Hindi public explanation

The LLM must not secretly override the MLP output.

9.4 Forecast Output Format

Use structured output:

{
  "model": "gold_mlp_v03",
  "date": "YYYY-MM-DD",
  "input_window": "last_60_days",
  "prediction": {
    "direction": "up",
    "strength": 0.72,
    "expected_range_percent": [1.2, 2.8]
  },
  "confidence": 0.64,
  "top_features": [
    "feature_1",
    "feature_2",
    "feature_3"
  ],
  "warning": "Not financial advice; model under validation."
}

Then the LLM may convert this into a human explanation.

10. Adapter Strategy

10.1 Use Separate Adapters

Do not mix unrelated tasks in one adapter.

Recommended adapters:

Adapter	Purpose
hindi_style_lora	pure Hindi / Sanskritized Hindi writing
mbfr_assistant_lora	MBFR explanation style and terminology
jyotisha_explainer_lora
sanskrit_grammar_lora
code_vb6_cpp_lora
geopolitical_synthesis_lora
wikidot_writer_lora

10.2 Do Not Merge Too Early

Separate adapters should remain separate unless:

their tasks are compatible
benchmark shows no degradation
the merged adapter is tested against all original tasks

Adapter conflict is real. A pure-Hindi adapter and a code adapter may not cooperate well. A Jyotiṣa adapter and MBFR adapter should not be merged unless needed.

10.3 Router-Based Loading

A small router should select the model/adapters:

User request
   |
   +-- Numeric forecast? ------> MLP / tabular model -> LLM explanation
   |
   +-- Jyotiṣa calculation? ---> VB6/C++ engine -> rule engine -> LLM explanation
   |
   +-- Sanskrit passage? ------> RAG + grammar tools -> LLM synthesis
   |
   +-- MBFR question? ---------> MBFR RAG -> LLM synthesis
   |
   +-- Code request? ----------> coding model / code adapter
   |
   +-- Pure Hindi essay? ------> Hindi style adapter + lexical filter
   |
   +-- General reasoning? -----> 30B–34B instruct model or Grok-2 if useful

11. Training Dataset Design

11.1 General Instruction Dataset

Format:

{
  "instruction": "Explain the following Jyotiṣa rule output in clear technical language.",
  "input": {
    "computed_factors": "...",
    "rules_applied": "...",
    "conflicts": "..."
  },
  "output": "..."
}

11.2 Pure Hindi Dataset

Each sample should include:

English/Hindi source
desired pure Hindi output
banned words if any
preferred alternatives
style level: simple, medium, scholarly
technical English terms allowed

Example:

{
  "instruction": "Rewrite in simple pure Hindi, avoiding Urdu/Persian/Arabic vocabulary.",
  "input": "The financial system exploits developing countries through dollar dominance.",
  "output": "वित्तीय तन्त्र डॉलर-प्रधान व्यवस्था द्वारा विकासशील देशों का आर्थिक शोषण करता है।",
  "constraints": {
    "avoid": ["system as सिस्टम if तन्त्र works", "तरीका", "जरिया"],
    "allow_english_terms": ["dollar", "financial"]
  }
}

11.3 Jyotiṣa Explanation Dataset

The model should not calculate. It should explain supplied calculations.

{
  "instruction": "Explain this computed Jyotiṣa result without inventing missing facts.",
  "input": {
    "lagna": "...",
    "varga_data": "...",
    "dasha": "...",
    "rules": [
      {"rule_id": "R001", "text": "...", "score": 0.72}
    ]
  },
  "output": {
    "summary": "...",
    "supporting_factors": ["..."],
    "opposing_factors": ["..."],
    "final_synthesis": "...",
    "confidence": "medium"
  }
}

11.4 MBFR Dataset

Use:

question
retrieved MBFR passages
calculation assumptions
answer
uncertainty
safety note

The model must distinguish between:

demonstrated fact
engineering assumption
proposed design
policy recommendation
speculation

12. Environment Setup

12.1 Operating Principle

Use separate environments. Do not pollute the system Python.

Recommended layout:

D:\AI\
   models\
      qwen3_32b\
      yi34b\
      mistral24b\
      grok2_archive_or_runtime\
   datasets\
      sanskrit\
      jyotisha\
      mbfr\
      hindi_style\
      code\
   adapters\
      hindi_style_lora\
      jyotisha_explainer_lora\
      mbfr_lora\
   rag\
      chroma_or_faiss\
      embeddings\
   scripts\
      train\
      infer\
      convert\
      evaluate\

12.2 Z890 Environment

Use Z890 for:

training
quantization
evaluation
large inference
RAG building

Suggested environments:

conda create -n llmtrain python=3.11 -y
conda activate llmtrain

pip install torch torchvision torchaudio
pip install transformers accelerate peft trl datasets bitsandbytes sentencepiece protobuf
pip install flash-attn --no-build-isolation
pip install deepspeed
pip install sentence-transformers faiss-cpu chromadb

Exact CUDA/Torch versions must match the installed RTX 5090 driver and working CUDA stack.

12.3 Z490 Environment

Use Z490 for stable inference and embeddings:

conda create -n llminfer python=3.11 -y
conda activate llminfer

pip install torch torchvision torchaudio
pip install transformers accelerate peft sentencepiece protobuf
pip install sentence-transformers faiss-cpu chromadb

12.4 Omen17 Environment

Use smaller stack:

conda create -n smallai python=3.11 -y
conda activate smallai

pip install torch torchvision torchaudio
pip install transformers accelerate sentencepiece protobuf
pip install llama-cpp-python

13. Model Evaluation

13.1 Never Trust One Demo

A model must be tested on:

easy prompts
hard prompts
adversarial prompts
Sanskrit terms
pure-Hindi vocabulary
MBFR technical questions
Jyotiṣa rule explanation
code generation
hallucination tests
refusal/over-caution tests
deterministic output tests

13.2 Benchmark Table

Maintain a table:

Model	Machine	Quant	Context	Tokens/sec	VRAM	RAM	Best Use	Problems
Grok-2 local 298GB	Z890	unknown/tested	?	?	?	?	experimental	to test
Qwen 32B	Z890/Z490	Q4/Q5/QLoRA	?	?	?	?	general + Hindi + code	to test
Yi 34B	Z890/Z490	Q4/Q5/QLoRA	?	?	?	?	reasoning + Sanskrit tests	to test
Mistral Small 24B	Z890/Z490	Q4/Q5	?	?	?	?	fast assistant	to test
7B–14B model	Omen17	Q4	?	?	?	?	portable assistant	limited depth

14. Deployment Architecture

14.1 Local Server Plan

Run services separately:

Service	Machine
Main LLM server	Z890
Embedding server
RAG/vector DB
MLP forecast API
VB6/C++ astrology engine
Web UI / router
Laptop access

14.2 Simple Router

The router receives a user query and calls the right backend.

Pseudo-logic:

def route_request(query):
    if is_numeric_forecast(query):
        result = call_mlp_forecast(query)
        return explain_with_llm(result)
 
    if is_jyotisha_calculation(query):
        chart = call_astrology_engine(query)
        rules = apply_jyotisha_rules(chart)
        passages = retrieve_classical_sources(rules)
        return explain_jyotisha(chart, rules, passages)
 
    if is_sanskrit_or_purana_query(query):
        passages = retrieve_sanskrit_corpus(query)
        return answer_from_passages(query, passages)
 
    if is_mbfr_query(query):
        passages = retrieve_mbfr_docs(query)
        return answer_mbfr(query, passages)
 
    if needs_pure_hindi(query):
        draft = call_hindi_adapter(query)
        return lexical_filter(draft)
 
    if is_code_query(query):
        return call_code_model(query)
 
    return call_general_llm(query)

15. Practical Priority Order

15.1 Phase 1: Stabilize Local Inference

Test 7B–14B model on Omen17
Test 24B–34B model on RTX 3090
Test 30B–34B model on dual RTX 5090
Benchmark Grok-2 298GB only after the above are stable

15.2 Phase 2: Build RAG

Sanskrit corpus RAG
MBFR RAG
Jyotiṣa rule RAG
code/documentation RAG
pure-Hindi lexical database

15.3 Phase 3: Build Dataset

instruction samples
rejected bad-output samples
pure-Hindi rewrites
MBFR Q&A
Jyotiṣa explanation samples
Sanskrit grammar explanation samples
code conversion samples

15.4 Phase 4: Train Adapters

Hindi style adapter
MBFR adapter
Jyotiṣa explanation adapter
Sanskrit grammar adapter
code adapter

15.5 Phase 5: Numeric ML

gold model
rainfall model
weather model
other strength-output models
connect their outputs to LLM explanation

15.6 Phase 6: Grok-2 Integration

Only after the system works:

benchmark Grok-2 local speed
compare Grok-2 with 30B–34B models
use Grok-2 for batch reasoning if useful
avoid making Grok-2 a dependency for daily work unless speed is acceptable

16. Final Working Principle

The best system is not one giant LLM.

The best system is:

deterministic engines where rules must be exact
MLP/tabular models where output is numeric
RAG where knowledge must be cited and retrieved
30B–34B local LLMs where reasoning and language are needed
Grok-2 as a large experimental model if it proves useful on my hardware
small models for routing, testing, and laptop work
human review for final intellectual responsibility

Therefore:

Use Z890 dual RTX 5090 as the main AI laboratory.
Use Z490 RTX 3090 as the stable inference/RAG/embedding server.
Use Omen17 RTX 3070 Ti as the portable controller and test machine.
Keep Grok-2 298 GB as a valuable local asset, but do not make the whole architecture depend on it until benchmarked.
For Jyotiṣa and Sanskrit, use rule-first architecture.
For gold/weather/rainfall, use MLP/tabular prediction first.
For essays, explanations, pure Hindi, and synthesis, use LLM + RAG + filters.

17. Alternatives: Other Large Downloadable Models for Mostly-Frozen Partial Fine-Tuning

Purpose of this section:
This section lists large downloadable full-weight or open-weight models, comparable in spirit to my local Grok-2 experiment, which may be suitable for mostly-frozen partial fine-tuning through PEFT, LoRA, QLoRA, adapter training, or similar methods.

This section is not about small 7B, 14B, or 30B models. It is about large models whose base weights can be kept mostly frozen while a small project-specific adapter is trained.

17.1 Main Principle

The purpose is not to fully fine-tune these huge models. Full fine-tuning of such models is generally impractical on my local hardware.

The practical method is:

download the model weights if license and storage permit
load the model in the lightest working quantized/offloaded backend
keep the base weights frozen
train only a small LoRA/PEFT adapter
save adapter weights separately
compare base-model output and adapter-enhanced output
never merge the adapter into the base model until extensive testing is complete

Important MoE warning:
For Mixture-of-Experts models, the number of active parameters reduces compute per token, but the model still has a much larger total weight bank. Therefore, a model with 22B or 32B active parameters may still require handling 235B, 355B, 671B, or even 1T total parameters through VRAM, RAM, NVMe offload, or expert-loading methods.

17.2 Hardware Assumption

The evaluation here is based on my actual machines:

Machine	Role
Z890-AI Top + dual RTX 5090 + 256 GB RAM	main machine for large-model inference, quantization, offloading, and small adapter experiments
Z490 + RTX 3090 + 128 GB RAM	support machine for dataset preparation, embeddings, RAG, smaller inference, evaluation
Omen17 + RTX 3070 Ti 8 GB + 64 GB RAM	portable controller, dataset editing, testing, remote operation

The Z890 dual RTX 5090 has 64 GB total VRAM, not 80 GB, not 96 GB. Therefore, any model designed for a single 80 GB H100-class GPU may still need quantization, CPU offload, NVMe offload, tensor parallelism, or a specialized backend on my system.

17.3 Practical Ranking for My Mostly-Frozen Adapter Experiments

The ranking below is not only by raw intelligence or benchmark fame. It is ranked by usefulness for my local partial fine-tuning experiments.

Rank	Model	Type	Approx. Scale	Practical Judgment
1	OpenAI gpt-oss-120b	MoE	117B total / 5.1B active	Best first non-Grok large PEFT target; large but comparatively practical
2	Qwen3-235B-A22B	MoE	235B total / 22B active	Best balance of size, quality, context, and practicality
3	GLM-4.5	MoE	355B total / 32B active	Serious agentic/coding/reasoning model; heavier but attractive
4	Llama 4 Maverick	MoE, multimodal	approx. 400B total / 17B active	Strong Meta model; useful but license/tooling complexity matters
5	DeepSeek-V3 / R1 family	MoE	671B total / 37B active	Excellent reasoning/coding, but heavy for local PEFT
6	Mistral Large 3	MoE, multimodal	675B total / 41B active	Excellent Apache-2.0 European open-weight option; very heavy
7	Kimi K2	MoE	1T total / 32B active	Very strong agentic/coding model; too heavy for first PEFT attempt
8	GLM-5 / GLM-5.1 class	MoE	around 744B–754B class / ~40B active	Frontier-scale, but likely a large infrastructure project
9	Tencent Hunyuan-Large	MoE	389B total / 52B active	Technically relevant and PEFT-friendly, but less attractive than newer models
10	Mistral Large 2 / 123B	Dense	123B dense	Strong dense baseline, but dense memory load is less convenient than MoE

Best practical sequence after Grok-2:
First test gpt-oss-120b, then Qwen3-235B-A22B, then GLM-4.5.
Only after these are stable should I attempt DeepSeek, Mistral Large 3, Kimi K2, GLM-5 class, or other 600B–1T models.

17.4 OpenAI gpt-oss-120b

My first recommended non-Grok large adapter target: gpt-oss-120b

Company: OpenAI
Architecture: MoE
Approximate scale: 117B total parameters, 5.1B active parameters
Main attraction: It is large enough to matter but much more practical than 235B–1T models.

It is suitable for:

pure-Hindi + Wikidot adapter
MBFR technical explanation adapter
Sanskrit/Purāṇa RAG answer-formatting adapter
local reasoning assistant
code/document explanation
comparison against Grok-2

Advantages:

large but comparatively manageable
official open-weight model
suitable for local experimentation
good first target before larger MoE models
likely easier than 235B, 355B, 671B, 675B, or 1T models

Cautions:

designed around a specific response format
may still need careful backend support
64 GB VRAM may require quantization/offload
adapter training must be tested slowly with a small dataset first

Verdict:
This is the best first alternative large-model PEFT experiment after my Grok-2 test.

17.5 Qwen3-235B-A22B

Best balance model: Qwen3-235B-A22B

Company: Alibaba / Qwen
Architecture: MoE
Approximate scale: 235B total parameters, 22B active parameters
Main attraction: It is a true large model but still much more practical than 600B–1T MoE models.

It is suitable for:

pure Hindi generation and correction
Sanskrit/Purāṇa explanation
MBFR assistant
long-context RAG synthesis
Wikidot formatting
code and research assistant
document restructuring

Advantages:

strong multilingual base
strong instruction-following
long-context variants exist
large but not absurdly large
good candidate for one focused adapter

Cautions:

total 235B weight bank is still very large
requires careful quantization/offloading
adapter training should begin with small tests
not suitable for numeric forecasting as the predictor

Verdict:
This is the best balance of scale and practicality for large local adapter work.

17.6 GLM-4.5

Best agentic/coding candidate after Qwen3-235B: GLM-4.5

Company: Z.ai / Zhipu AI
Architecture: MoE
Approximate scale: 355B total parameters, 32B active parameters
Main attraction: Strong reasoning, coding, and agentic abilities.

It is suitable for:

coding assistant
MBFR technical reasoning
multi-step document planning
research synthesis
structured Wikidot/HTML generation
agent-style local workflows

Advantages:

serious large model
agentic design
strong coding and reasoning orientation
more practical than GLM-5 class models

Cautions:

still a very large model
355B total parameters require substantial storage and offload strategy
likely harder than gpt-oss-120b and Qwen3-235B
best attempted after one successful large-model adapter experiment

Verdict:
A high-priority candidate, but not the first one to attempt.

17.7 Llama 4 Maverick

Useful large Western MoE model: Llama 4 Maverick

Company: Meta
Architecture: MoE, multimodal
Approximate scale: about 400B total parameters, 17B active parameters
Main attraction: Large Meta model with strong ecosystem support.

It is suitable for:

general reasoning
multilingual text
code and document work
multimodal experiments
comparison against Grok-2, Qwen, GLM, and DeepSeek

Advantages:

large but active-parameter count is relatively low
Meta ecosystem support
potentially strong multimodal capability
useful benchmark model

Cautions:

license terms must be checked carefully before redistribution or derivative release
multimodal architecture may complicate text-only adapter experiments
not necessarily the cleanest model for my first PEFT test

Verdict:
Worth testing, but not the first choice for my adapter work.

17.8 DeepSeek-V3 / DeepSeek-R1 Family

Strong reasoning/coding family: DeepSeek-V3 / DeepSeek-R1

Company: DeepSeek
Architecture: MoE
Approximate scale: 671B total parameters, 37B active parameters
Main attraction: Strong reasoning, coding, mathematical, and technical capability.

It is suitable for:

reasoning comparison
code generation
MBFR technical reasoning
synthetic dataset generation
hard problem analysis
comparison against Grok-2 and Qwen

Advantages:

excellent reasoning reputation
strong coding ability
useful for generating training examples
valuable as a comparison model

Cautions:

very large total parameter count
difficult for local PEFT on 64 GB VRAM without serious offload
should not be first adapter target
may be better first used for inference and dataset generation

Verdict:
Excellent model family, but too heavy for first local PEFT attempt.

17.9 Mistral Large 3

Best European large open-weight candidate: Mistral Large 3

Company: Mistral AI
Architecture: granular MoE, multimodal
Approximate scale: 675B total parameters, 41B active parameters
Main attraction: Strong open-weight European model, Apache-2.0 release, suitable for serious customization where feasible.

It is suitable for:

MBFR writing and technical explanation
multilingual technical synthesis
long-context RAG
image + text document interpretation where supported
high-quality essay and policy drafting
comparison against Grok-2, DeepSeek, Qwen, and GLM

Advantages:

strong open-weight model
clean licensing compared with many alternatives
large context and multimodal orientation
enterprise-grade design

Cautions:

675B total parameters is a huge load
requires serious serving/offload backend
adapter training is not a first experiment
should be attempted only after gpt-oss-120b / Qwen3 / GLM-4.5 experience

Verdict:
Very important model, but not the first practical adapter target.

17.10 Kimi K2

Very strong but very heavy: Kimi K2

Company: Moonshot AI
Architecture: MoE
Approximate scale: 1T total parameters, 32B active parameters
Main attraction: Large agentic and coding-oriented model, useful for frontier-class open-weight comparison.

It is suitable for:

agentic coding
long-horizon project planning
codebase reasoning
document synthesis
comparison against Grok-2
generating high-quality synthetic examples

Advantages:

extremely large model
strong coding and agentic orientation
only 32B active parameters per token
important model to track

Cautions:

1T total parameter bank is a huge storage and serving problem
not a first adapter-training candidate
local PEFT may become a hardware/debugging trap
use only after easier large models are under control

Verdict:
Download-worthy if storage permits and official weights are available, but not first for training.

17.11 GLM-5 / GLM-5.1 Class

Frontier-class but infrastructure-heavy: GLM-5 / GLM-5.1

Company: Z.ai / Zhipu AI
Architecture: MoE
Approximate scale: around 744B–754B class, about 40B active class
Main attraction: Very large agentic engineering model family.

It is suitable for:

advanced coding
agentic engineering
long technical reasoning
high-end comparison against Grok-2 and DeepSeek
large document workflows

Advantages:

newer and larger than GLM-4.5
serious agentic engineering orientation
large open-weight direction
potentially excellent for coding and project planning

Cautions:

likely too large for simple local adapter work
requires advanced backend support
not suitable as first non-Grok PEFT model
should be treated as a later-stage infrastructure experiment

Verdict:
Important future candidate, but GLM-4.5 is the more practical first GLM target.

17.12 Tencent Hunyuan-Large

Technically relevant but lower priority: Tencent Hunyuan-Large

Company: Tencent
Architecture: MoE
Approximate scale: 389B total parameters, 52B active parameters
Main attraction: Large MoE model with explicit relevance to fine-tuning and local research.

It is suitable for:

Chinese/English technical assistant
MoE fine-tuning reference
comparison against Qwen, GLM, DeepSeek
large-context research tasks

Advantages:

large MoE model
high active parameter count
useful for studying MoE training/inference patterns
technically relevant to PEFT discussion

Cautions:

not as attractive now as newer Qwen/GLM/DeepSeek/Kimi models
52B active parameters may be heavy
not the best first target for my hardware
ecosystem momentum may be weaker than Qwen, DeepSeek, GLM, or Mistral

Verdict:
Worth knowing, but not a priority download unless a specific reason arises.

17.13 Mistral Large 2 / 123B Dense

Strong dense baseline: Mistral Large 2 / 123B

Company: Mistral AI
Architecture: dense transformer
Approximate scale: 123B dense parameters
Main attraction: Simpler architecture than MoE models, strong general-purpose capability.

It is suitable for:

dense-model comparison
technical writing
coding
RAG synthesis
multilingual explanation
adapter experiments where dense architecture is preferred

Advantages:

simpler than MoE
strong general-purpose model
useful dense baseline
avoids some MoE backend complexity

Cautions:

dense 123B means direct memory load is heavy
active parameter count is not reduced as in MoE
may be less efficient than MoE alternatives on my hardware
not the first choice unless dense architecture is specifically desired

Verdict:
Good comparison model, but not the main path for my large PEFT experiments.

17.14 Best Adapter Projects for These Large Models

For all of these huge models, the first adapter project should be linguistic, structural, or explanatory. It should not be numeric prediction.

Best first adapter projects:

Priority	Adapter Project	Why It Is Suitable
1	Pure Hindi + Wikidot formatting	easy to evaluate, useful immediately, low risk
2	MBFR technical explanation	controlled terminology and style, useful for website/policy pages
3	Sanskrit/Purāṇa RAG answer formatting	model explains retrieved passages without needing to memorize corpus
4	Jyotiṣa explanation-only adapter	explains computed outputs but does not calculate or predict independently
5	VB6/C++ code assistant style	useful for my actual codebase and DLL/VB6 workflows

Do not start with: gold price prediction, rainfall prediction, weather magnitude prediction, or deterministic Jyotiṣa phala generation.
Those should remain under MLP/tabular models, classical rule engines, and computed feature pipelines. The LLM should explain the result, not secretly generate the result.

17.15 Practical Testing Order

Recommended testing sequence:

Grok-2 local copy: one small mostly-frozen adapter experiment
gpt-oss-120b: first alternative large PEFT experiment
Qwen3-235B-A22B: best balance large MoE
GLM-4.5: serious agentic/coding/reasoning model
Llama 4 Maverick: Meta ecosystem comparison
DeepSeek-V3 / R1: high-reasoning heavy model
Mistral Large 3: high-quality Apache-2.0 frontier-scale model
Kimi K2: 1T-scale agentic model, later only
GLM-5 / GLM-5.1 class: later infrastructure experiment
Tencent Hunyuan-Large or Mistral Large 2: special-purpose comparison

This sequence avoids wasting time by jumping directly into 600B–1T models before the adapter pipeline is proven.

17.16 Evaluation Checklist for Any Alternative Large Model

Before training an adapter, record:

Test Item	What to Record
folder size	total GB after download
file format	safetensors, GGUF, FP8, MXFP4, NVFP4, etc.
tokenizer	tokenizer type and chat template
backend	Transformers, vLLM, SGLang, llama.cpp, KTransformers, etc.
license	whether commercial use, redistribution, derivative adapters are allowed
context length	tested context length, not only advertised length
VRAM use	one 5090, two 5090s, RTX 3090 if tested
RAM use	system RAM at load and during inference
disk activity	whether HDD/NVMe is being hammered
tokens/sec	prompt processing and generation speed
output quality
adapter effect
failure modes	hallucination, formatting break, Hindi impurity, code errors, etc.

No model should be accepted merely because it is famous or huge.

17.17 Final Recommendation

My practical large-model path:

Use my local Grok-2 copy first for one mostly-frozen adapter test.
Then test gpt-oss-120b as the most practical alternative large model.
Then test Qwen3-235B-A22B as the best balance of scale and usability.
Then test GLM-4.5 for agentic coding and reasoning.
Treat DeepSeek, Mistral Large 3, Kimi K2, and GLM-5 class models as later high-end experiments.
Keep numeric forecasting and deterministic Jyotiṣa calculation outside the LLM.
Use LLM adapters for explanation, style, formatting, synthesis, and controlled domain language.

The correct architecture remains:

Exact computation / retrieval / prediction
        ↓
Structured facts
        ↓
Large mostly-frozen model + small adapter
        ↓
Clear explanation / pure Hindi / Wikidot / MBFR / Sanskrit synthesis

page revision: 3, last edited: 29 Apr 2026 03:17

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Vedic Astrology

Vedic Jyotisha Unravelled !

Local AI Strategy for Z890-AI Top, Z490 RTX 3090, and Omen17 RTX 3070 Ti

1. Corrected Starting Point

1.1 What Must Not Be Assumed

1.2 What Can Be Assumed

2. Hardware Roles

2.1 Z890-AI Top + Dual RTX 5090 + 256 GB RAM

2.2 Z490 + RTX 3090 + 128 GB RAM

2.3 Omen17 + RTX 3070 Ti 8 GB + 64 GB RAM

3. Model Strategy: Do Not Use One Model for Everything

3.1 Recommended Role Division

4. Grok-2: Mostly Frozen Adapter Use

4.1 What “Mostly Frozen Grok-2” Means

4.2 Why This Is Useful

4.3 Best First Grok-2 Adapter Project

4.4 Practical Training Method

4.5 Suggested Adapter Settings

4.6 Dataset Format

4.7 Important Boundary

4.8 Hardware Use

4.9 Final Rule for Grok-2

5. Serious Rule-Based Work: Use 30B–34B Local Models, Not Grok-2 First

5.1 Suitable Domains

5.2 Why 30B–34B Is the Practical Sweet Spot

5.3 Candidate Model Families

5.4 Full Fine-Tuning vs QLoRA

6. PhalitaGPT and Jyotiṣa: Correct Architecture

6.1 Do Not Let an LLM Invent Predictions

6.2 PhalitaGPT Layer Separation

6.3 Output Discipline

7. Sanskrit Grammar and Pure Hindi

7.1 Sanskrit Grammar

7.2 Pure Hindi Generation

8. RAG for Sanskrit, Purāṇas, MBFR, and Research

8.1 Why RAG Is Essential

8.2 Sanskrit/Purāṇa RAG

8.3 MBFR RAG

9. Numeric Prediction: Gold, Rainfall, Weather, etc.

9.1 LLM Is Not the Predictor

9.2 Recommended ML Models

9.3 Correct LLM Role in Forecasting

9.4 Forecast Output Format

10. Adapter Strategy

10.1 Use Separate Adapters

10.2 Do Not Merge Too Early

10.3 Router-Based Loading

11. Training Dataset Design

11.1 General Instruction Dataset

11.2 Pure Hindi Dataset

11.3 Jyotiṣa Explanation Dataset

11.4 MBFR Dataset

12. Environment Setup

12.1 Operating Principle

12.2 Z890 Environment

12.3 Z490 Environment

12.4 Omen17 Environment

13. Model Evaluation

13.1 Never Trust One Demo

13.2 Benchmark Table

14. Deployment Architecture

14.1 Local Server Plan

14.2 Simple Router

15. Practical Priority Order

15.1 Phase 1: Stabilize Local Inference

15.2 Phase 2: Build RAG

15.3 Phase 3: Build Dataset

15.4 Phase 4: Train Adapters

15.5 Phase 5: Numeric ML

15.6 Phase 6: Grok-2 Integration

16. Final Working Principle

17. Alternatives: Other Large Downloadable Models for Mostly-Frozen Partial Fine-Tuning

17.1 Main Principle

17.2 Hardware Assumption

17.3 Practical Ranking for My Mostly-Frozen Adapter Experiments

17.4 OpenAI gpt-oss-120b

17.5 Qwen3-235B-A22B

17.6 GLM-4.5

17.7 Llama 4 Maverick

17.8 DeepSeek-V3 / DeepSeek-R1 Family