Instructions to use AMAImedia/NOESIS-Gemma3-1B-BF16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AMAImedia/NOESIS-Gemma3-1B-BF16 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="AMAImedia/NOESIS-Gemma3-1B-BF16")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AMAImedia/NOESIS-Gemma3-1B-BF16")
model = AutoModelForCausalLM.from_pretrained("AMAImedia/NOESIS-Gemma3-1B-BF16", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AMAImedia/NOESIS-Gemma3-1B-BF16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AMAImedia/NOESIS-Gemma3-1B-BF16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AMAImedia/NOESIS-Gemma3-1B-BF16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AMAImedia/NOESIS-Gemma3-1B-BF16

SGLang

How to use AMAImedia/NOESIS-Gemma3-1B-BF16 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "AMAImedia/NOESIS-Gemma3-1B-BF16" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AMAImedia/NOESIS-Gemma3-1B-BF16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "AMAImedia/NOESIS-Gemma3-1B-BF16" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AMAImedia/NOESIS-Gemma3-1B-BF16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AMAImedia/NOESIS-Gemma3-1B-BF16 with Docker Model Runner:
```
docker model run hf.co/AMAImedia/NOESIS-Gemma3-1B-BF16
```

Released as part of the NOESIS Professional Multilingual Dubbing Automation Platform (framework: DHCF-FNO — Deterministic Hybrid Control Framework for Frozen Neural Operators).

Founder: Ilia Bolotnikov
Organization: AMAImedia.com
X (Twitter): @AMAImediacom
LinkedIn: Ilia Bolotnikov
Telegram: @djbionicl
NOESIS version: v16.1
Release date: 2026-08

================================================================================ NOESIS-Gemma3-1B-BF16 -- NOESIS Bundle README

NOESIS LoRA-merged Scenema student (1B class). Authoritative BF16 master for the lightweight Scenema-DiT audio cross-attention text encoder (recipe-validation companion / smoke-test student for the primary 4B Scenema student bundle).

NOESIS provenance

Bundle : NOESIS-Gemma3-1B-BF16 Parent bundle : NOESIS-3.5B-A0.5B-DUBBING-FILM / LTX2.3-5B-Scenema-Audio-DiT-BF16 Folder lineage : Renamed 2026-06-04 (was NOESIS-Gemma3-1B-PhaseE-Merged-BF16; renamed per v4-archive folder-rationalization pass). Upstream base : google/gemma-3-1b-it (Google LLC, Gemma Terms of Use) NOESIS variant : NOESIS LoRA-merged Scenema student fine-tune. PEFT merge_and_unload of NOESIS cinema r=8 + autoif-v2 r=16 LoRA adapters into the BF16 base. Fully merged single-shard model.safetensors; no residual adapter layers. NOESIS role : Recipe-validation companion / lightweight test student for the primary NOESIS-Gemma3-4B-Scenema-LTX2.3-BF16 student. Used for Phase 1D scenema_adapter recipe smoke-tests before scaling to 4B. Production Scenema text-encoder path uses the 4B sibling; this 1B is the validation + experimentation slot. NOESIS version : v15.9 Last updated : 2026-05-28

Founder : Ilia Bolotnikov Organization : AMAImedia.com (https://www.amaimedia.com) X (Twitter) : https://x.com/AMAImediacom LinkedIn : https://www.linkedin.com/in/ilia-bolotnikov Telegram : https://t.me/AMAImediacom

================================================================================ NOESIS Fine-Tune Lineage

google/gemma-3-1b-it
    © Google LLC 2024-2025 (Gemma Terms of Use)
        │
        │  NOESIS cinema LoRA r=8 (60K pairs)
        │  + autoif-v2 LoRA r=16
        │  PEFT merge_and_unload → BF16
        ▼
NOESIS-Gemma3-1B-BF16  ← THIS BUNDLE
    © AMAImedia 2026 (NOESIS fine-tune derivative)
    Apache-2.0 for the NOESIS LoRA delta + packaging;
    Gemma 3 base weights inherit Google Gemma Terms of Use.

The Scenema audio cross-attention pipeline needs 3840-d per-token text embeddings (R-SCENEMA-KD-DIM-3840-NOT-5376). The 1B student's hidden state is ~1152-d; the separately trained scenema_adapter.pt Linear projection (1152 → 3840) bridges to the Scenema-DiT audio_aggregate_embed layout — same pattern as the 4B sibling, just with the smaller backbone for recipe validation.

================================================================================ Architecture

Property	Value
Architecture	`Gemma3ForConditionalGeneration` (1B IT variant)
Total parameters	~1B
hidden_size	~1152
num_hidden_layers	~26
num_attention_heads	4
num_key_value_heads	1 (GQA 4:1)
vocab_size	262,144 (Gemma 3 tokenizer)
Precision	BF16 (fully merged, no adapters)
File size	~1.86 GB (single shard `model.safetensors`)
Scenema adapter target dim	3840 (R-SCENEMA-KD-DIM-3840-NOT-5376)
Adapter projection	Linear(1152 → 3840), separately trained

================================================================================ Bundle Contents

.
├── README.md
├── LICENSE                      # Apache-2.0 + NOESIS notice + Gemma Terms
├── config.json                  # Gemma3ForConditionalGeneration config
├── generation_config.json
├── chat_template.jinja
├── tokenizer.json
├── tokenizer_config.json
└── model.safetensors            # BF16 weights (~1.86 GB, single shard)

================================================================================ NOESIS Scenema Student Family

Bundle	Role	Size	Hidden
Gemma3-12B-it-Qat-Q4_0-Unquantized-BF16	KD teacher	~23.4 GB	3840 (KD target dim)
NOESIS-Gemma3-4B-Scenema-LTX2.3-BF16	Primary student	~8.01 GB	2560 → 3840 via adapter
NOESIS-Gemma3-1B-BF16 ← this bundle	Recipe-validation student	~1.86 GB	~1152 → 3840 via adapter
NOESIS-Gemma4-12B-it-Qat-Q4_0-Unquantized-BF16	Route-2 G4→G3 adapter experiments	~23.9 GB	3840 (different distribution)

================================================================================ NOESIS Sealed Rules

R-APACHE-CLEAN NOESIS LoRA delta + packaging released under Apache-2.0. Upstream Gemma 3 1B base weights remain under Google Gemma Terms of Use — redistribution must include attribution to Google and a copy of the Gemma Terms (see LICENSE NOTICE).

R-VENDORED-INTERNAL Internal vendor copy inside parent NOESIS-3.5B-A0.5B-DUBBING-FILM / LTX2.3-5B-Scenema-Audio-DiT-BF16.

R-SCENEMA-KD-DIM-3840-NOT-5376 (sealed 2026-05-21) Scenema KD target dim = 3840 (Gemma 3 12B teacher hidden_size). This 1B student's hidden states (~1152) are projected to 3840 via a separately trained scenema_adapter.pt Linear head. Legacy 5376 is WRONG and must never be used.

R-SCENEMA-PER-TOKEN-NOT-MEAN-POOL (sealed 2026-05-21) Adapter output must be per-token [seq, 3840]. Mean-pool to [1, 3840] breaks Scenema-DiT audio cross-attention.

R-CINEMA-LORA-MERGED (sealed 2026-05-26) Cinema LoRA r=8 permanently merged into model.safetensors via PEFT merge_and_unload. No adapter files remain in this bundle.

R-AUTOIF-LORA-MERGED (sealed 2026-05-26) autoif-v2 LoRA r=16 permanently merged into model.safetensors via PEFT merge_and_unload. No adapter files remain in this bundle.

R-BF16-SOURCE-OF-TRUTH-NO-QUANT (sealed 2026-05-26) BF16 master, NOT dequantized from any downstream quant. All future re-quantization / distillation / fine-tuning must start here.

R-DUBBING-FILM-SCOPE (sealed 2026-04-29) NOESIS = professional audio dubbing. This bundle is the 1B-class recipe-validation companion for the Scenema audio text-encoder pipeline.

R-NOESIS-FINAL-ARTIFACT-PATHS (sealed 2026-05-27) Canonical path: models/llm/NOESIS-3.5B-A0.5B-DUBBING-FILM/ LTX2.3-5B-Scenema-Audio-DiT-BF16/NOESIS-Gemma3-1B-BF16/

R-NEVER-DELETE-WITHOUT-EXPLICIT-CONSENT (sealed 2026-05-21) MUST NOT be deleted without explicit operator instruction "удали

================================================================================ Upstream Citation

Upstream base: https://huggingface.co/google/gemma-3-1b-it Gemma Terms: https://ai.google.dev/gemma/terms

================================================================================

NOESIS — Deterministic Hybrid Control Framework for Frozen Neural Operators (DHCF-FNO). Copyright (c) 2026 AMAImedia.com. All rights reserved. Gemma 3 1B base weights © 2024-2025 Google LLC, released under the Gemma Terms of Use (https://ai.google.dev/gemma/terms).

======================================== Scenema bridge v6 PAIRED (2026-06-06)

scenema_adapter_v3.pt = bridge_1b_v6.pt (ScenemaAdapterV3, 1152->188160, val_cos 0.8953). NOT weight-merged into model.safetensors (non-linear LayerNorm+GELU projector applied to OUTPUT hidden states, mathematically cannot fold into transformer weights). Loaded as paired runtime by scenema_wrapper.ScenemaEncoder.from_pretrained(this_dir): Gemma3-1B forward -> hidden[1152] -> ScenemaAdapterV3 -> Scenema-DiT [seq x 188160]. Plateau at ~0.895 (beats v4 baseline 0.88); en weakest 0.858, ko strong 0.965. Source: LORA/bridges_v6_2026-06-05/.