Instructions to use OsaurusAI/Nemotron-Labs-Audex-2B-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use OsaurusAI/Nemotron-Labs-Audex-2B-4bit with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("OsaurusAI/Nemotron-Labs-Audex-2B-4bit") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use OsaurusAI/Nemotron-Labs-Audex-2B-4bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "OsaurusAI/Nemotron-Labs-Audex-2B-4bit"
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "OsaurusAI/Nemotron-Labs-Audex-2B-4bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use OsaurusAI/Nemotron-Labs-Audex-2B-4bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "OsaurusAI/Nemotron-Labs-Audex-2B-4bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "OsaurusAI/Nemotron-Labs-Audex-2B-4bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OsaurusAI/Nemotron-Labs-Audex-2B-4bit", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use OsaurusAI/Nemotron-Labs-Audex-2B-4bit with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "OsaurusAI/Nemotron-Labs-Audex-2B-4bit"
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default OsaurusAI/Nemotron-Labs-Audex-2B-4bit
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use OsaurusAI/Nemotron-Labs-Audex-2B-4bit with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "OsaurusAI/Nemotron-Labs-Audex-2B-4bit"
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "OsaurusAI/Nemotron-Labs-Audex-2B-4bit" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Configuration Parsing Warning:Config file tokenizer_config.json cannot be fetched (too big)
Nemotron-Labs-Audex-2B-4bit
MLX affine 4-bit, group-size-64 conversion of
nvidia/Nemotron-Labs-Audex-2B
at exact revision d43e996bab673833ffb56dcfcc5b658f229f7343.
Status: PASS for the explicitly selected instruct audio-input/text-output path; PARTIAL for the complete upstream feature set. The language decoder is quantized. The NV-Whisper audio encoder and audio projector stay in source precision.
Runtime pull requests:
Until those changes merge into a release, use the PR revisions rather than an older packaged vMLX/Osaurus build.
Proven local row
The exact directory uploaded to this repository passed both static bundle validation and live Apple-silicon generation on 2026-07-20.
- Model class:
nemotron_dense_audex - Weight shards: 3
- Weight bytes: 2567319984 (2.391 GiB)
- Indexed tensors: 1057
- Quantized modules: 170
- Source-precision audio tensors: 490
- Load time: 4.8 seconds
- Three turn rates: 117.1 / 119.6 / 114.7 tok/s
- Current sampled physical footprint: 3.868 GiB
- Peak sampled physical footprint: 6.427 GiB
- Stop result: normal stop on all three turns
- Visible output: coherent on all three turns
- Reasoning/control-marker leakage: none in the passing instruct row
The live input was a real speech WAV. Turn 1 transcribed it. Turn 2 correctly named vegetables from the transcript. Turn 3 correctly confirmed that mutton was mentioned. The 96-token setting was a safety maximum; a length stop was a failure condition and did not occur.
The sampled footprint values are direct telemetry, not a low-RAM claim.
Required template mode
NVIDIA's template supports thinking and instruct modes. The passing runtime row explicitly selected instruct mode at request time:
let input = UserInput(
chat: [
.user(
"Transcribe the speech accurately.",
audios: [.url(audioURL)])
],
additionalContext: ["enable_thinking": false]
)
This is the source template's own <think></think> instruct path. It is not a
modified model default, forced close token, prompt rewrite, or sampler guard.
Important negative result: with enable_thinking omitted, representative 2B
and 30B rows stopped after emitting the transcription inside an unclosed
reasoning channel. The production stream reported 195-196 reasoning characters
and zero visible characters. Default-thinking audio is therefore not claimed
as passing. Hosts should expose the instruct choice explicitly and keep that
thinking-mode row marked partial.
Generation parameters
The proof used the bundle generation defaults:
temperature = 0.6
top_p = 1.0
top_k = 0
min_p = 0.0
repetition_penalty = nil
NVIDIA separately recommends greedy decoding for ASR/translation and temperature 0.7 with top-p 0.9 for audio understanding. Those are explicit task recipes; a host must not silently install them as family-wide defaults.
Download
hf download OsaurusAI/Nemotron-Labs-Audex-2B-4bit \
--local-dir ~/models/OsaurusAI/Nemotron-Labs-Audex-2B-4bit
Static verification
From the vMLX PR checkout:
python3 scripts/verify-audex-bundle.py \
~/models/OsaurusAI/Nemotron-Labs-Audex-2B-4bit \
--expect-bits 4 \
--source /path/to/Nemotron-Labs-Audex-2B/checkpoint_folder_full
The verifier checks every safetensors header against the index, tensor
uniqueness and shard ownership, exact byte totals, complete affine triplets,
incomplete files, and the Nemotron-H stacked expert layout when applicable.
With --source, it also compares every audio tensor's dtype, shape, and raw
data SHA-256 to the exact source snapshot; that stronger mode passed before
this repository was uploaded.
Architecture and cache ownership
- Cache topology: 28 standard causal KV caches.
- Audio: 32-layer NV-Whisper/Qwen2 encoder, 128 mel bins, 16 kHz input.
- Audio embeddings: 750 per 30-second clip.
- Output: text tokens only in the current native Swift runtime.
No prefix/paged/L2-disk/TurboQuant-KV hit is claimed by this release row. A future cache claim must include counters, coherent post-hit output, andโfor the 30B familyโthe exact Mamba companion-state restore boundary.
Supported and unsupported surfaces
| Surface | Status |
|---|---|
| Text plus audio -> text | Proven in explicit instruct mode |
| Growing audio conversation -> text | Proven in explicit instruct mode |
| Default-thinking audio -> visible text | Failed representative row |
| Image/video input | Unsupported by this wrapper |
| Batch size greater than one | Unsupported; use batch size 1 |
| Text-to-speech output | Not implemented in this Swift runtime |
| Text-to-audio output | Not implemented in this Swift runtime |
| Speech-to-speech audio output | Not implemented in this Swift runtime |
The upstream repository includes separate audio-generation checkpoints and codecs. Their presence does not make this input-audio quant an output-audio runtime.
Conversion provenance
{
"format": "vmlx-affine-audex",
"source_model": "nvidia/Nemotron-Labs-Audex-2B",
"source_revision": "d43e996bab673833ffb56dcfcc5b658f229f7343",
"source_model_type": "nemotron_dense_audex",
"quantization": {
"method": "affine",
"bits": 4,
"group_size": 64,
"language_decoder": "quantized",
"audio_encoder": "source_precision",
"audio_projector": "source_precision"
},
"conversion_counts": {
"affine_simple_tensors": 170,
"affine_expert_groups": 0,
"passthrough_tensors": 547,
"dropped_runtime_unused_tensors": 0
},
"weight_shards": 3,
"weight_bytes": 2567319984
}
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TTS, ํ
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License
Use is governed by the NVIDIA OneWay Noncommercial License. LICENSE.txt and
the source-family license/ directory are included. Review
license/THIRD_PARTY_NOTICES.md before redistribution or deployment.
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