license: mit library_name: gguf tags:

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

Pwdz-1 Uncensored

Pwdz-1 Uncensored 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, tuned for unrestricted, refusal-free output. This repository ships Pwdz-1 as a ready-to-run GGUF with its vision projector included, so text and image input work out of the box.

Highlights

Uncensored — tuned to answer directly across the full range of requests, without the usual hedging and refusals
Multimodal by design — the bundled mmproj activates the vision tower; attach images directly, no extra assembly
1M token context — native long-context support for huge documents, codebases, and long agent runs
MoE efficiency — sparse activation keeps generation fast despite the model's scale
Single-file GGUF — one weight file + one projector, nothing else to wire up
Thinking mode — supports reasoning-effort control via chat template kwargs

Repository contents

File	Size	Role
`Pwdz-1.gguf`	137 GB	Main model weights (Q4-class quant)
`mmproj-Pwdz-1-BF16.gguf`	1.1 GB	Vision projector (multimodal tower)

Requirements

A recent llama.cpp build with glm5-next support (builds from October 2026 onward)
~145 GB of combined VRAM/RAM for full load — e.g. 2 × 96 GB GPUs runs it comfortably fully on GPU
Disk: ~140 GB free

Quickstart — llama.cpp

llama-server \
  -m Pwdz-1.gguf \
  --mmproj mmproj-Pwdz-1-BF16.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-1-Uncensored",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Vision usage

curl http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "pwdz/Pwdz-1-Uncensored",
    "messages": [{
      "role": "user",
      "content": [
        {"type": "text", "text": "Describe this image in detail."},
        {"type": "image_url", "image_url": {"url": "data:image/png;base64,<base64>"}}
      ]
    }]
  }'

Or via CLI:

llama-mtmd-cli -m Pwdz-1.gguf --mmproj mmproj-Pwdz-1-BF16.gguf \
  --image photo.jpg -p "What is in this image?"

Unsloth Studio

Pwdz-1 Uncensored 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

Reasoning effort is exposed through the chat template:

"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-1 Uncensored

Highlights

Uncensored — tuned to answer directly across the full range of requests, without the usual hedging and refusals

Multimodal by design — the bundled mmproj activates the vision tower; attach images directly, no extra assembly

1M token context — native long-context support for huge documents, codebases, and long agent runs

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

Single-file GGUF — one weight file + one projector, nothing else to wire up

Thinking mode — supports reasoning-effort control via chat template kwargs

File

Size

Role

Pwdz-1.gguf

137 GB

Main model weights (Q4-class quant)

mmproj-Pwdz-1-BF16.gguf

1.1 GB

Vision projector (multimodal tower)

Quickstart — llama.cpp

llama-server \ -m Pwdz-1.gguf \ --mmproj mmproj-Pwdz-1-BF16.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-1-Uncensored", "messages": [{"role": "user", "content": "Hello!"}] }'

Vision usage

curl http://localhost:8080/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "pwdz/Pwdz-1-Uncensored", "messages": [{ "role": "user", "content": [ {"type": "text", "text": "Describe this image in detail."}, {"type": "image_url", "image_url": {"url": "data:image/png;base64,<base64>"}} ] }] }'

Or via CLI:

llama-mtmd-cli -m Pwdz-1.gguf --mmproj mmproj-Pwdz-1-BF16.gguf \ --image photo.jpg -p "What is in this image?"