Code-Writer-V2-Obliterated AWQ Mixed (4-bit)

Overview

Code-Writer-V2-Obliterated-AWQ-Mixed-Lean is a weight-quantized checkpoint intended for efficient GPU inference, published by groxaxo. It is intended for open-source evaluation, reproducible experimentation, and compatible local or hosted inference workflows. The wording below is deliberately limited to what can be verified from this repository's metadata and artifacts.

The repository name identifies a behavior-modified or reduced-filtering lineage. That label describes the source or conversion history; it is not a guarantee of unrestricted behavior in every prompt or runtime. Test outputs carefully before sharing or deploying them.

At a glance

Field	Details
Format	AWQ / AutoRound
Source / base	`groxaxo/Code-Writer-V2-Obliterated-BF16`
Intended task	`image-text-to-text`
License	`other`

What is included

*.safetensors (9 files)
config.json
generation_config.json
tokenizer.json
tokenizer_config.json
processor_config.json
chat_template.jinja
quantization_config.json
Additional configuration, tokenizer, processor, or shard files (18 visible artifacts total)

Quick start

vLLM (AWQ-compatible runtimes)

vllm serve groxaxo/Code-Writer-V2-Obliterated-AWQ-Mixed-Lean \
  --quantization awq_marlin \
  --dtype float16 \
  --trust-remote-code

The exact kernel and flags depend on the quantizer and architecture. Check the files and source model card before selecting a production serving configuration.

Compatibility and responsible use

Use a runtime that explicitly supports this format, architecture, and modality.
Keep configuration, tokenizer, processor, projection, and weight files from the same revision together.
Review the source model card and license before redistribution or deployment.
Hardware needs depend on parameter count, context length, cache precision, quantization, and concurrency.
Report reproducible issues with the runtime version, hardware, launch command, and a minimal example.

Quantization or conversion changes numerical behavior, memory use, and throughput relative to the source checkpoint; validate quality on your own workload.

Generated outputs may be inaccurate or unsuitable for a given use case. Users are responsible for testing behavior, applying appropriate safeguards, and complying with applicable licenses and laws.

AWQ 4-bit mixed-precision quant of groxaxo/Code-Writer-V2-Obliterated-BF16 optimized for vLLM serving on 2× RTX 3090 (24 GB each, compute capability 8.6).

This is the format that actually serves on vLLM for the qwen3_5 hybrid-attention architecture. The AutoRound-native (auto_round / GPTQ-packing) format produces degenerate output (only token-id-0 / !) under vLLM 0.23's loader; the same checkpoints generate correctly in plain transformers. AWQ is the only format vLLM serves correctly for this architecture. Full build log and reproduction recipe: github.com/groxaxo/codewriter-v2-awq-quant-guide.

Quantization recipe

Field	Value
Format	`awq` (gemm), `zero_point=True` (asymmetric)
Bits / group	4 / 128
Algorithm	AWQ activation-aware smoothing + RTN, calibration-free (`--model_free`)
Tool	`auto-round 0.13.1` → `--format auto_awq`
Modules kept in BF16	vision tower, all GDN `linear_attn.in_proj_{qkv,z,a,b}`, full-attn `self_attn.{q,k,v}_proj`, layer 0, `lm_head`

Benchmark

Metric	This model	Published AutoRound-W4A16-RTN baseline
PIQA acc (n=500)	0.814 ±0.0174	0.8074
PIQA acc_norm (n=500)	0.832 ±0.0167	—

Beats the existing RTN baseline on the directly-comparable benchmark despite being calibration-free (the AWQ activation-aware smoothing does real work).

Serving (vLLM 0.23, the exact command that loads)

vllm serve groxaxo/Code-Writer-V2-Obliterated-AWQ-Mixed-Lean \
  --served-model-name codewriter-v2-awq \
  --tensor-parallel-size 2 \
  --gpu-memory-utilization 0.92 \
  --max-model-len 32768 \
  --max-num-seqs 1 \
  --max-num-batched-tokens 8192 \
  --enable-chunked-prefill \
  --disable-custom-all-reduce \
  --dtype float16

The three non-obvious flags are required on RTX 3090:

--dtype float16 — vLLM rejects bfloat16 for AWQ
--disable-custom-all-reduce — the custom all-reduce kernel crashes on cc 8.6
--max-num-seqs 1 + small --max-num-batched-tokens — default profile OOMs during CUDA-graph capture

VRAM at idle: ~21 GB / GPU. Throughput at --max-num-seqs 1 is conservative; raise it if you have KV-cache headroom.

Architecture (verified from config)

model_type: qwen3_5, Qwen3_5ForConditionalGeneration (VLM — vision tower present)
64 layers: 16 full-attention (every 4th) + 48 GDN/linear-attention
num_key_value_heads = 4 → tensor-parallel must be 2 or 4, never 3
No MTP head → never pass --speculative-config
Text model path: model.language_model.layers.N.*