license: mit library_name: gguf tags:

gguf
llama.cpp
abliterated
heretic
uncensored
vision
multimodal
moe
1m-context base_model: zai-org/GLM-5.3-Flash pipeline_tag: image-text-to-text

Pwdz-2 Abliterated

Pwdz-2 Abliterated is a multimodal large language model built on the GLM-5.3-Flash architecture — a 320B-class sparse Mixture-of-Experts with a native vision tower and a 1,048,576-token context window, abliterated to strip out the refusal direction: the model answers everything directly while keeping its full reasoning and instruction-following ability intact. This repository ships Pwdz-2 as a ready-to-run GGUF in a space-efficient IQ4_XS-class dynamic quant, with its F16 vision projector included — text and image input work out of the box.

Highlights

Abliterated — refusal behavior removed via weight-space ablation, not prompt hacks: no system-prompt games needed, and no lobotomized answers
Multimodal by design — the bundled F16 mmproj activates the vision tower; attach images directly
1M token context — native long-context for massive documents, codebases, and agent workflows
Dynamic quantization — per-tensor intelligent quant allocation keeps quality high at a smaller footprint
MoE efficiency — sparse activation keeps generation fast despite the model's scale
Thinking mode — reasoning-effort control via chat template kwargs

Repository contents

File	Size	Role
`Pwdz-2-00001-of-00005.gguf` … `00005-of-00005`	~157 GB total	Main model weights (5-shard split GGUF, IQ4_XS-class)
`mmproj-Pwdz-2-F16.gguf`	1.1 GB	Vision projector (F16 multimodal tower)

Split GGUF: point llama.cpp at the first shard (-00001-of-00005) — the rest are resolved automatically from the same directory.

Requirements

A recent llama.cpp build with glm5-next support (builds from October 2026 onward)
~165 GB of combined VRAM/RAM for full load — 2 × 96 GB GPUs runs it fully on GPU
Disk: ~160 GB free

Quickstart — llama.cpp

llama-server \
  -m Pwdz-2-00001-of-00005.gguf \
  --mmproj mmproj-Pwdz-2-F16.gguf \
  -ngl 999 \
  -c 131072 \
  --port 8080

Then chat (OpenAI-compatible):

curl http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "pwdz/Pwdz-2-Abliterated",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Vision usage

curl http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "pwdz/Pwdz-2-Abliterated",
    "messages": [{
      "role": "user",
      "content": [
        {"type": "text", "text": "Describe this image in detail."},
        {"type": "image_url", "image_url": {"url": "data:image/png;base64,<base64>"}}
      ]
    }]
  }'

Unsloth Studio

Pwdz-2 Abliterated drops straight into Unsloth Studio: the model appears in the local model picker, the vision projector is auto-detected and attached at load, and image input is enabled in chat immediately.

Thinking control

"chat_template_kwargs": {"reasoning_effort": "high"}

Use "enable_thinking": false for fast direct answers.

License

MIT — inherited from the GLM-5.3-Flash base architecture.

Pwdz-2 Abliterated

Highlights

Abliterated — refusal behavior removed via weight-space ablation, not prompt hacks: no system-prompt games needed, and no lobotomized answers

Multimodal by design — the bundled F16 mmproj activates the vision tower; attach images directly

1M token context — native long-context for massive documents, codebases, and agent workflows

Dynamic quantization — per-tensor intelligent quant allocation keeps quality high at a smaller footprint

MoE efficiency — sparse activation keeps generation fast despite the model's scale

Thinking mode — reasoning-effort control via chat template kwargs

Repository contents

File

Size

Role

Pwdz-2-00001-of-00005.gguf … 00005-of-00005

~157 GB total

Main model weights (5-shard split GGUF, IQ4_XS-class)

mmproj-Pwdz-2-F16.gguf

1.1 GB

Vision projector (F16 multimodal tower)

Split GGUF: point llama.cpp at the first shard (-00001-of-00005) — the rest are resolved automatically from the same directory.

Quickstart — llama.cpp

llama-server \ -m Pwdz-2-00001-of-00005.gguf \ --mmproj mmproj-Pwdz-2-F16.gguf \ -ngl 999 \ -c 131072 \ --port 8080

Then chat (OpenAI-compatible):

curl http://localhost:8080/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "pwdz/Pwdz-2-Abliterated", "messages": [{"role": "user", "content": "Hello!"}] }'

Vision usage

curl http://localhost:8080/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "pwdz/Pwdz-2-Abliterated", "messages": [{ "role": "user", "content": [ {"type": "text", "text": "Describe this image in detail."}, {"type": "image_url", "image_url": {"url": "data:image/png;base64,<base64>"}} ] }] }'