XReyRobert/Nex-N2-mini-GPTQ-Pro

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Nex-N2-mini GPTQ-Pro

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This is a GPTQ-Pro 4-bit quantization of `nex-agi/Nex-N2-mini`.

It is a deployment artifact, not a new fine-tune. The goal is to make the Nex-N2-mini MoE checkpoint easier to test in GPTQ-compatible local serving stacks while keeping the model card honest about the validation status.

The source checkpoint includes vision/visual tensors. This artifact preserves those tensors, but the validated publication story here is text and coding-agent serving. Vision behavior has not yet been validated for the quantized artifact.

Source And Credits

Source model:

• —`nex-agi/Nex-N2-mini`

Quantization tooling and reference recipe:

• —`groxaxo/GPTQ-Pro`
• —`groxaxo/Qwen3.6-27B-GPTQ-Pro-4bit`

Artifact Summary

The source index/logs showed no mtp.* tensors. This artifact therefore normalizes text_config.mtp_num_hidden_layers to 0 and records the change under artifact_notes.mtp.

Quantization Recipe

Fallback smoothing was enabled for difficult groups with threshold 0.5%.

Intended Serving Shape

This checkpoint is intended for advanced users testing text-only GPTQ serving for Qwen3.6-style MoE models.

A starting vLLM shape for text-only testing:

vllm serve XReyRobert/Nex-N2-mini-GPTQ-Pro \
  --served-model-name nex-n2-mini-gptq-pro \
  --language-model-only \
  --dtype float16 \
  --quantization gptq_marlin \
  --tensor-parallel-size 1 \
  --max-model-len 262144 \
  --max-num-seqs 1 \
  --kv-cache-dtype fp8_e5m2 \
  --reasoning-parser qwen3 \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_coder \
  --enable-prefix-caching \
  --gpu-memory-utilization 0.95 \
  --trust-remote-code

Serving context for the published Smoke24/vLLM measurements:

The Smoke24/vLLM numbers were collected on an internal llm-residency vLLM deployment. The custom image recipe is not published yet, so this card does not present that image as a public reproduction target. The stable serving knobs captured from the run are listed for context.

Treat this as a starting point. Loader compatibility depends on vLLM, Transformers, GPTQModel, GPTQ-Marlin, and Qwen3.6 MoE support.

The RTX 3090 image above reflects separate 262k-context serving validation.

Public vLLM Reproducibility

This artifact has a public reproducibility path on the unmodified upstream vLLM OpenAI image:

• —image: docker.io/vllm/vllm-openai:nightly-7a1eb8ac2ec4ea69338c51dc7afd4b15010abfa8
• —vLLM version observed in validation: 0.20.1rc1.dev16+g7a1eb8ac2
• —GPU class: single RTX 3090 24 GB / Ampere
• —--enforce-eager was not used
• —no local sleep/wake patch or localhost/*sleepwake* image is required for the validation below

Validated serving shapes:

• —--max-model-len 131072 validated with --gpu-memory-utilization 0.94
• —--max-model-len 262144 validated with --gpu-memory-utilization 0.96
• —--language-model-only, --dtype float16, --quantization gptq_marlin
• —--kv-cache-dtype fp8_e5m2, --enable-prefix-caching, --max-num-seqs 1
• —--max-num-batched-tokens 2096, --max-cudagraph-capture-size 32
• —--reasoning-parser qwen3, --tool-call-parser qwen3_coder

The 262k profile is tight on 24 GB GPUs; the lower 0.94 memory target used for 131k was short on KV cache, while 0.96 passed.

Validation And Benchmarks

Completed artifact checks:

• —Local shard index inspection completed before upload.
• —Remote file list verified after upload.
• —Remote model.safetensors.index.json verified after upload.
• —Index metadata total size matches the local safetensor shards.
• —The remote artifact contains the expected five safetensor shards.

Terminal-Bench 2.0 Smoke24 result and associated vLLM serving measurements. This Smoke24 run used max_model_len=131072 for apples-to-apples comparison with the other local models in this publication batch:

Smoke24 is a fixed 24-task Terminal-Bench 2.0 comparison corpus, not a full Terminal-Bench leaderboard run. In this harness, Nex-N2-mini GPTQ-Pro tied the Qwen3.6 27B GPTQ reference on solved tasks but used more wall time and far more output tokens. That makes it a useful candidate for further serving and generation-control tuning, not an efficiency leader in this specific test.

Task list and harness shape:

• —`benchmarks/terminal-bench-2.0/smoke24_task_list_20260616.md`

MTP And Vision Status

• —mtp.* tensors are not present in this artifact.
• —text_config.mtp_num_hidden_layers was normalized to 0.
• —Do not enable MTP speculative decoding for this artifact.
• —Vision/visual tensors are present, but multimodal serving has not been validated for this quantized artifact.

Limitations

• —Experimental quantization.
• —Terminal-Bench Smoke24 is a small local comparison corpus, not a full benchmark submission.
• —Nex-N2-mini was verbose and reasoning-heavy in the Smoke24 harness; generation controls may need further tuning.
• —MTP speculative decoding is not supported by this artifact.
• —Vision tensors are preserved, but vision behavior has not been validated.
• —Loader behavior may vary across vLLM, Transformers, GPTQModel, and GPTQ-Marlin versions.

Files

Key files:

• —model.safetensors.index.json
• —model-00001-of-00005.safetensors through model-00005-of-00005.safetensors
• —config.json
• —quantize_config.json
• —processor_config.json
• —tokenizer.json
• —UPLOAD_MANIFEST.json

UPLOAD_MANIFEST.json records the upload guardrail checks and artifact inspection summary.

References

• —Source model: `nex-agi/Nex-N2-mini`
• —GPTQ-Pro tooling: `groxaxo/GPTQ-Pro`
• —Reference recipe: `groxaxo/Qwen3.6-27B-GPTQ-Pro-4bit`
• —Terminal-Bench: `laude-institute/terminal-bench`

Individual Project Notice

This repository is an individual research project. It is not affiliated with, sponsored by, or endorsed by any employer or organization.