gnani-evon-v3.3-30B-A3B API & Inference Endpoint

Model Overview

Model Developer: Gnani.ai

gnani-evon-v3.3

Supported languages: English, Hindi, Bengali, Telugu, Tamil, Marathi, Gujarati, Kannada, Malayalam, Odia, and Punjabi.

Release date: August 2026

gnani-evon-v3.3 is a hybrid Mamba architecture 30B MOE A3B model trained end-to-end for best-in-class Indic capability — continued pretraining, supervised fine-tuning, and reinforcement learning — delivering deep comprehension, extractive QA, and reasoning across Hindi, Bengali, Telugu, Tamil, Marathi, Gujarati, Kannada, Malayalam, Odia, Punjabi, and English. Developed by Gnani.ai, each stage was oriented toward native-script Indic language understanding and instruction following. It supports 128K context length.

The objective for this release was best-in-class Indic capability. gnani-evon-v3.3 concentrates on Indic-language comprehension, extraction, and reasoning depth, giving us a strong foundation to build on in upcoming releases.

Model Architecture

Architecture Type: Mamba2-Transformer Hybrid Mixture of Experts (MoE)
Network Architecture: Nemotron Hybrid MoE (nemotron_h)
Parameters: 30B total / ~3.5B active per token
Maximum context length: 131,072 tokens (128K)
Precision: BF16

Key takeaways

MILU: gnani-evon-v3.3 leads Sarvam-30B on all 11 of 11 languages at matched scale, and Sarvam-105B (≈3.5× larger) on 10 of 11
We beat gpt-5.6-luna, a hosted frontier model, on MILU Physics (91.32 vs 88.14) — the benchmark's largest subject at 4,435 items.
Statistical parity with gpt-5.4-nano on Indic comprehension — 79.46 against 79.69 on the nine-language macro, a 0.23-point difference, and ahead on four of those nine languages plus English.
Indic extractive QA is our strongest competitive position: we lead every open-weight comparator on XQuAD-in (64.59 F1, +9.50) and XorQA-in (41.83 F1, +7.23).

MILU — exact scores by language

gnani-evon-v3.3 tops this MILU breakdown — ahead of Sarvam-30B on every language and ahead of Sarvam-105B on 10 of 11.

Table with columns: Language, gnani-evon-v3.3, Sarvam-30B, Sarvam-105B
Language	gnani-evon-v3.3	Sarvam-30B	Sarvam-105B
English	84.06	71.13	79.73
Kannada	82.44	71.35	78.89
Bengali	82.30	69.81	78.53
Hindi	81.99	67.08	76.95

Intended Use

gnani-evon-v3.3 is a general-purpose instruction-following, reasoning, and agentic model intended for:

English and Indic language chat and assistant applications
Multilingual RAG systems with long context (up to 128K)
AI agent systems with tool calling and multi-turn reasoning
Code and mathematical reasoning
Instruction following in English and Indic languages

Not recommended for: high-stakes decisions, legal/medical/financial advice, or deployment without safety evaluation on your target languages and domains.

Deployment geography: Global

Input / Output

Input: Text — chat messages (OpenAI-compatible roles: system, user, assistant, tool)
Output: Text — string, optionally with a reasoning trace
Maximum input / output size: 131,072 tokens
Supported languages: English, Hindi, Bengali, Telugu, Tamil, Marathi, Gujarati, Kannada, Malayalam, Odia, Punjabi

Software Integration

Runtime Engine(s): Transformers ≥ 5.3.0, vLLM ≥ 0.12.0, SGLang
Supported Hardware: NVIDIA H100 80GB, NVIDIA H200, NVIDIA A100
Operating System(s): Linux

Quick Start Guide

Use it with Transformers

Requires trust_remote_code=True

python

import torchfrom transformers import AutoTokenizer, AutoModelForCausalLM model_id = "gnani/gnani-evon-v3.3" tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained(    model_id,    torch_dtype=torch.bfloat16,    trust_remote_code=True,    device_map="auto",) messages = [    {"role": "user", "content": "Explain photosynthesis in Hindi."},] tokenized_chat = tokenizer.apply_chat_template(    messages,    tokenize=True,    add_generation_prompt=True,    return_tensors="pt",).to(model.device) outputs = model.generate(    tokenized_chat,    max_new_tokens=1024,    temperature=0.6,    top_p=0.95,    eos_token_id=tokenizer.eos_token_id,)print(tokenizer.decode(outputs[0]))

Use temperature=0.6 and top_p=0.95 for best results across reasoning, tool calling, and general instruction following.

To disable reasoning traces, pass enable_thinking=False to apply_chat_template.

Use it with vLLM

bash

pip install -U "vllm>=0.12.0" vllm serve gnani/gnani-evon-v3.3 \  --served-model-name model \  --max-model-len 131072 \  --port 8000 \  --trust-remote-code \  --enable-auto-tool-choice \  --tool-call-parser qwen3_coder \  --reasoning-parser nano_v3

Example client request:

shell

curl http://localhost:8000/v1/chat/completions \    -H "Content-Type: application/json" \    -d '{        "model": "model",        "messages": [{"role": "user", "content": "Write a haiku about monsoon rains in Tamil."}],        "max_tokens": 4096    }'

To disable reasoning with vLLM, add "chat_template_kwargs": {"enable_thinking": false} to the request body.

SGLang

bash

pip install "sglang[all]" ninja

Start the server:

bash

python -m sglang.launch_server \    --model-path gnani/gnani-evon-v3.3 \    --served-model-name gnani-evon-v3.3 \    --dtype bfloat16 \    --context-length 131072 \    --mem-fraction-static 0.90 \    --moe-runner-backend triton \    --host 0.0.0.0 --port 30000

--moe-runner-backend triton selects the Triton mixture of experts kernels instead of the CUTLASS ones. The CUTLASS path is compiled on first use and that build needs a CUDA toolchain whose compiler and headers are the same version, which is not the case in many pip-only installs. The Triton path needs no such build and starts cleanly. Drop the flag if your CUDA toolchain is consistent and you want the CUTLASS kernels.

Add --tp 2 or higher for longer context or higher concurrency. Lower --mem-fraction-static if the server runs out of memory while capturing graphs.

Send a request to the OpenAI-compatible endpoint:

bash

curl http://localhost:30000/v1/chat/completions \  -H 'Content-Type: application/json' \  -d '{    "model": "gnani-evon-v3.3",    "messages": [      {"role": "user", "content": "ಸೌರಶಕ್ತಿಯ ಮೂರು ಉಪಯೋಗಗಳನ್ನು ತಿಳಿಸಿ."}    ],    "max_tokens": 2048,    "temperature": 0.6,    "top_p": 0.95,    "chat_template_kwargs": {"enable_thinking": true}  }'

Recommended Inference Settings

Two request configurations cover most use cases. Choose based on whether the task needs multi-step reasoning.

Deterministic single answer — factual QA, classification, short structured replies (e.g. multiple-choice, one-line answers). Reasoning off, near-greedy:

json

{  "model": "model",  "messages": [    {"role": "system", "content": "Respond in the same language and script as the question."},    {"role": "user", "content": "..."}  ],  "temperature": 0.6,  "top_p": 0.95,  "repetition_penalty": 1.1,  "max_tokens": 2048,  "chat_template_kwargs": { "enable_thinking": false },  "stream": false}

Reasoning — math, physics, multi-step problems where accuracy matters more than latency. Reasoning on, with temperature=0.6 and top_p=0.95:

json

{  "model": "model",  "messages": [    {"role": "system", "content": "Respond in the same language and script as the question."},    {"role": "user", "content": "..."}  ],  "temperature": 0.6,  "top_p": 0.95,  "repetition_penalty": 1.0,  "max_tokens": 16384,  "chat_template_kwargs": { "enable_thinking": true },  "stream": false}

Notes:

Non-reasoning mode is faster but slightly less accurate on prompts that require multi-step reasoning; enable thinking for those.
Avoid combining a hard no_repeat_ngram_size with repetition_penalty on very short answers — legitimate repetition (units, option letters) can be blocked, forcing awkward phrasing.
For strictly native-script output, keep a system instruction that names the target language and script, and verify the response script downstream.

Training Overview

gnani-evon-v3.3 was built specifically for Indic capabilities — comprehension, extraction, reasoning, and instruction following across English and Indic languages. Every training stage was oriented toward that goal:

Pretraining on a blended English + Indic corpus to strengthen multilingual foundations and long-context Indic representation.
Supervised fine-tuning (SFT) on instruction and capability-focused datasets covering QA, summarization, safety, and multi-turn dialogue in native scripts.
Reinforcement learning (GRPO) to sharpen reasoning quality, refusal behavior, and task completion on Indic-heavy prompts.

Training ran on H200 GPU clusters using NVIDIA's NeMo software stack: NeMo Curator, Megatron Bridge, and NeMo RL.

Data collection & labeling: Hybrid — automated, human, and synthetic.

Evaluation

Indic Benchmarks

Indic Frontier Models Comparison

Table with columns: Benchmark, Metric, gnani-evon-v3.3, Sarvam-30B, Sarvam-105B
Benchmark	Metric	gnani-evon-v3.3	Sarvam-30B	Sarvam-105B
milu	accuracy	78.74	67.15	75.71
indicmmlu_pro	accuracy	68.79	—	—
bharatmath	accuracy	73.97	60.32	—

Open Source Models Comparison

Table with columns: Benchmark, Metric, gnani-evon-v3.3, Sarvam-30B, Sarvam-105B, gemma-4-26B-A4B, gemma-4-31B
Benchmark	Metric	gnani-evon-v3.3	Sarvam-30B	Sarvam-105B	gemma-4-26B-A4B	gemma-4-31B
milu	accuracy	78.74	67.15	75.71	82.65	85.29
indicmmlu_pro	accuracy	68.79	—	—	70.34

Safety and Security Evaluation

Evaluated on IndicSafe — 5,965 rows across 12 Indic languages.

Overall accuracy: 86.50%

Accuracy by Harm Category (%)

Table with columns: Harm Category, gnani-evon-v3.3
Harm Category	gnani-evon-v3.3
Harmless Control	97.93
Tricky Ambiguous	97.77
Offensive / Hate Speech	89.80
Harmful Instructions	85.62
Misinformation / Conspiracy	83.50
Health Misinformation	80.41
Religious Hate / Provocation	80.08
Political Manipulation	72.49
Gender & Caste Discrimination

Key observations:

Strongest categories: Harmless Control (97.93%) and Tricky Ambiguous (97.77%).
Misinformation / Conspiracy (83.50%) is a relative strength among harm categories.
Gender & Caste Discrimination (70.26%) is the hardest category.

MILU by Language — vs Hosted Models (identical items & prompts)

Table with columns: Language, gnani-evon-v3.3, gpt-5.4-nano, DeepSeek-V4-Flash
Language	gnani-evon-v3.3	gpt-5.4-nano	DeepSeek-V4-Flash
Hindi	81.99	82.29	76.02
English	84.06	83.05	78.93
Telugu	79.78	79.52	71.15
Bengali	82.30	81.63	76.44

Limitations & Ongoing Work

Tool use and agentic capabilities are an active area of development. We are working to improve multi-step tool use to match frontier-model capabilities at a similar model size.

Ethical Considerations

Evaluate the model on their target languages, domains, and use cases before deployment.
Implement appropriate safety guardrails for production use.
Be aware of potential biases in web-scraped and synthetic training data.
Not use the model for high-stakes decisions without human oversight.
Ensure any voice or conversational agent built on this model discloses that it is AI.

Looking ahead: this model is the Indic-capability foundation for our next plan, where we focus on improving the model further — layering in strong agentic capabilities such as multi-step tool use, planning, and orchestration.

Citation

bibtex

@misc{gnani_evon_v33_2026,  title  = {{gnani-evon-v3.3}: Hybrid {Mamba} {MoE} 30B-A3B Model Trained End-to-End for Best-in-Class Indic Capability},  author = {{Gnani.ai}},  year   = {2026},  url    = {https://huggingface.co/gnani/gnani-evon-v3.3}}

Model Overview

Model Developer: Gnani.ai

gnani-evon-v3.3

Supported languages: English, Hindi, Bengali, Telugu, Tamil, Marathi, Gujarati, Kannada, Malayalam, Odia, and Punjabi.

Release date: August 2026

Model Architecture

Architecture Type: Mamba2-Transformer Hybrid Mixture of Experts (MoE)
Network Architecture: Nemotron Hybrid MoE (nemotron_h)
Parameters: 30B total / ~3.5B active per token
Maximum context length: 131,072 tokens (128K)
Precision: BF16

Key takeaways

MILU: gnani-evon-v3.3 leads Sarvam-30B on all 11 of 11 languages at matched scale, and Sarvam-105B (≈3.5× larger) on 10 of 11
We beat gpt-5.6-luna, a hosted frontier model, on MILU Physics (91.32 vs 88.14) — the benchmark's largest subject at 4,435 items.
Statistical parity with gpt-5.4-nano on Indic comprehension — 79.46 against 79.69 on the nine-language macro, a 0.23-point difference, and ahead on four of those nine languages plus English.
Indic extractive QA is our strongest competitive position: we lead every open-weight comparator on XQuAD-in (64.59 F1, +9.50) and XorQA-in (41.83 F1, +7.23).

MILU — exact scores by language

gnani-evon-v3.3 tops this MILU breakdown — ahead of Sarvam-30B on every language and ahead of Sarvam-105B on 10 of 11.

Table with columns: Language, gnani-evon-v3.3, Sarvam-30B, Sarvam-105B
Language	gnani-evon-v3.3	Sarvam-30B	Sarvam-105B
English	84.06	71.13	79.73
Kannada	82.44	71.35	78.89
Bengali	82.30	69.81	78.53
Hindi	81.99	67.08	76.95

Intended Use

gnani-evon-v3.3 is a general-purpose instruction-following, reasoning, and agentic model intended for:

English and Indic language chat and assistant applications
Multilingual RAG systems with long context (up to 128K)
AI agent systems with tool calling and multi-turn reasoning
Code and mathematical reasoning
Instruction following in English and Indic languages

Not recommended for: high-stakes decisions, legal/medical/financial advice, or deployment without safety evaluation on your target languages and domains.

Deployment geography: Global

Input / Output

Input: Text — chat messages (OpenAI-compatible roles: system, user, assistant, tool)
Output: Text — string, optionally with a reasoning trace
Maximum input / output size: 131,072 tokens
Supported languages: English, Hindi, Bengali, Telugu, Tamil, Marathi, Gujarati, Kannada, Malayalam, Odia, Punjabi

Software Integration

Runtime Engine(s): Transformers ≥ 5.3.0, vLLM ≥ 0.12.0, SGLang
Supported Hardware: NVIDIA H100 80GB, NVIDIA H200, NVIDIA A100
Operating System(s): Linux

Quick Start Guide

Use it with Transformers

Requires trust_remote_code=True

python

import torchfrom transformers import AutoTokenizer, AutoModelForCausalLM model_id = "gnani/gnani-evon-v3.3" tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained(    model_id,    torch_dtype=torch.bfloat16,    trust_remote_code=True,    device_map="auto",) messages = [    {"role": "user", "content": "Explain photosynthesis in Hindi."},] tokenized_chat = tokenizer.apply_chat_template(    messages,    tokenize=True,    add_generation_prompt=True,    return_tensors="pt",).to(model.device) outputs = model.generate(    tokenized_chat,    max_new_tokens=1024,    temperature=0.6,    top_p=0.95,    eos_token_id=tokenizer.eos_token_id,)print(tokenizer.decode(outputs[0]))

Use temperature=0.6 and top_p=0.95 for best results across reasoning, tool calling, and general instruction following.

To disable reasoning traces, pass enable_thinking=False to apply_chat_template.

Use it with vLLM

bash

pip install -U "vllm>=0.12.0" vllm serve gnani/gnani-evon-v3.3 \  --served-model-name model \  --max-model-len 131072 \  --port 8000 \  --trust-remote-code \  --enable-auto-tool-choice \  --tool-call-parser qwen3_coder \  --reasoning-parser nano_v3

Example client request:

shell

curl http://localhost:8000/v1/chat/completions \    -H "Content-Type: application/json" \    -d '{        "model": "model",        "messages": [{"role": "user", "content": "Write a haiku about monsoon rains in Tamil."}],        "max_tokens": 4096    }'

To disable reasoning with vLLM, add "chat_template_kwargs": {"enable_thinking": false} to the request body.

SGLang

bash

pip install "sglang[all]" ninja

Start the server:

bash

python -m sglang.launch_server \    --model-path gnani/gnani-evon-v3.3 \    --served-model-name gnani-evon-v3.3 \    --dtype bfloat16 \    --context-length 131072 \    --mem-fraction-static 0.90 \    --moe-runner-backend triton \    --host 0.0.0.0 --port 30000

Add --tp 2 or higher for longer context or higher concurrency. Lower --mem-fraction-static if the server runs out of memory while capturing graphs.

Send a request to the OpenAI-compatible endpoint:

bash

curl http://localhost:30000/v1/chat/completions \  -H 'Content-Type: application/json' \  -d '{    "model": "gnani-evon-v3.3",    "messages": [      {"role": "user", "content": "ಸೌರಶಕ್ತಿಯ ಮೂರು ಉಪಯೋಗಗಳನ್ನು ತಿಳಿಸಿ."}    ],    "max_tokens": 2048,    "temperature": 0.6,    "top_p": 0.95,    "chat_template_kwargs": {"enable_thinking": true}  }'

Recommended Inference Settings

Two request configurations cover most use cases. Choose based on whether the task needs multi-step reasoning.

Deterministic single answer — factual QA, classification, short structured replies (e.g. multiple-choice, one-line answers). Reasoning off, near-greedy:

json

{  "model": "model",  "messages": [    {"role": "system", "content": "Respond in the same language and script as the question."},    {"role": "user", "content": "..."}  ],  "temperature": 0.6,  "top_p": 0.95,  "repetition_penalty": 1.1,  "max_tokens": 2048,  "chat_template_kwargs": { "enable_thinking": false },  "stream": false}

Reasoning — math, physics, multi-step problems where accuracy matters more than latency. Reasoning on, with temperature=0.6 and top_p=0.95:

json

{  "model": "model",  "messages": [    {"role": "system", "content": "Respond in the same language and script as the question."},    {"role": "user", "content": "..."}  ],  "temperature": 0.6,  "top_p": 0.95,  "repetition_penalty": 1.0,  "max_tokens": 16384,  "chat_template_kwargs": { "enable_thinking": true },  "stream": false}

Notes:

Non-reasoning mode is faster but slightly less accurate on prompts that require multi-step reasoning; enable thinking for those.
Avoid combining a hard no_repeat_ngram_size with repetition_penalty on very short answers — legitimate repetition (units, option letters) can be blocked, forcing awkward phrasing.
For strictly native-script output, keep a system instruction that names the target language and script, and verify the response script downstream.

Training Overview

Pretraining on a blended English + Indic corpus to strengthen multilingual foundations and long-context Indic representation.
Supervised fine-tuning (SFT) on instruction and capability-focused datasets covering QA, summarization, safety, and multi-turn dialogue in native scripts.
Reinforcement learning (GRPO) to sharpen reasoning quality, refusal behavior, and task completion on Indic-heavy prompts.

Training ran on H200 GPU clusters using NVIDIA's NeMo software stack: NeMo Curator, Megatron Bridge, and NeMo RL.

Data collection & labeling: Hybrid — automated, human, and synthetic.

Evaluation

Indic Benchmarks

Indic Frontier Models Comparison

Table with columns: Benchmark, Metric, gnani-evon-v3.3, Sarvam-30B, Sarvam-105B
Benchmark	Metric	gnani-evon-v3.3	Sarvam-30B	Sarvam-105B
milu	accuracy	78.74	67.15	75.71
indicmmlu_pro	accuracy	68.79	—	—
bharatmath	accuracy	73.97	60.32	—

Open Source Models Comparison

Table with columns: Benchmark, Metric, gnani-evon-v3.3, Sarvam-30B, Sarvam-105B, gemma-4-26B-A4B, gemma-4-31B
Benchmark	Metric	gnani-evon-v3.3	Sarvam-30B	Sarvam-105B	gemma-4-26B-A4B	gemma-4-31B
milu	accuracy	78.74	67.15	75.71	82.65	85.29
indicmmlu_pro	accuracy	68.79	—	—	70.34

Safety and Security Evaluation

Evaluated on IndicSafe — 5,965 rows across 12 Indic languages.

Overall accuracy: 86.50%

Accuracy by Harm Category (%)

Table with columns: Harm Category, gnani-evon-v3.3
Harm Category	gnani-evon-v3.3
Harmless Control	97.93
Tricky Ambiguous	97.77
Offensive / Hate Speech	89.80
Harmful Instructions	85.62
Misinformation / Conspiracy	83.50
Health Misinformation	80.41
Religious Hate / Provocation	80.08
Political Manipulation	72.49
Gender & Caste Discrimination

Key observations:

Strongest categories: Harmless Control (97.93%) and Tricky Ambiguous (97.77%).
Misinformation / Conspiracy (83.50%) is a relative strength among harm categories.
Gender & Caste Discrimination (70.26%) is the hardest category.

MILU by Language — vs Hosted Models (identical items & prompts)

Table with columns: Language, gnani-evon-v3.3, gpt-5.4-nano, DeepSeek-V4-Flash
Language	gnani-evon-v3.3	gpt-5.4-nano	DeepSeek-V4-Flash
Hindi	81.99	82.29	76.02
English	84.06	83.05	78.93
Telugu	79.78	79.52	71.15
Bengali	82.30	81.63	76.44

Limitations & Ongoing Work

Tool use and agentic capabilities are an active area of development. We are working to improve multi-step tool use to match frontier-model capabilities at a similar model size.

Ethical Considerations

Evaluate the model on their target languages, domains, and use cases before deployment.
Implement appropriate safety guardrails for production use.
Be aware of potential biases in web-scraped and synthetic training data.
Not use the model for high-stakes decisions without human oversight.
Ensure any voice or conversational agent built on this model discloses that it is AI.

Citation

bibtex

@misc{gnani_evon_v33_2026,  title  = {{gnani-evon-v3.3}: Hybrid {Mamba} {MoE} 30B-A3B Model Trained End-to-End for Best-in-Class Indic Capability},  author = {{Gnani.ai}},  year   = {2026},  url    = {https://huggingface.co/gnani/gnani-evon-v3.3}}

gnani-evon-v3.3-30B-A3B

README

Model Overview

gnani-evon-v3.3

Model Architecture

Key takeaways

MILU — exact scores by language

Intended Use

Input / Output

Software Integration

Quick Start Guide

Use it with Transformers

Use it with vLLM

SGLang

Recommended Inference Settings

Training Overview

Evaluation

Indic Benchmarks

Indic Frontier Models Comparison

Open Source Models Comparison

Safety and Security Evaluation

Accuracy by Harm Category (%)

MILU by Language — vs Hosted Models (identical items & prompts)

Limitations & Ongoing Work

Ethical Considerations

Citation

Explore FriendliAI today

README

Model Overview

gnani-evon-v3.3

Model Architecture

Key takeaways

MILU — exact scores by language

Intended Use

Input / Output

Software Integration

Quick Start Guide

Use it with Transformers

Use it with vLLM

SGLang

Recommended Inference Settings

Training Overview

Evaluation

Indic Benchmarks

Indic Frontier Models Comparison

Open Source Models Comparison

Safety and Security Evaluation

Accuracy by Harm Category (%)

MILU by Language — vs Hosted Models (identical items & prompts)

Limitations & Ongoing Work

Ethical Considerations

Citation