---
license: apache-2.0
base_model:
- Qwen/Qwen3.5-9B
pipeline_tag: image-text-to-text
library_name: transformers
tags:
- blackfrost
- the-void
- pinqwen
- 1m-context
- uncensored
- reasoning
- long-context
- coding
- vision-language
---
# PINQWEN‑3.5‑9B‑1M
> ### 🪟 **A 9B that reads a *million* tokens, thinks before it speaks, and never refuses.**
**PINQWEN‑3.5‑9B‑1M** is Blackfrost AI's compact powerhouse — a reasoning model distilled through **The Void**, our multi‑teacher reasoning‑distillation method, on the **Qwen 3.5 9B** architecture. It reasons first and answers second: for any real problem it opens a `` block, works it through, then delivers a clean, direct answer — and it does it across a **full one‑million‑token context window** that most models this size can only dream of.
---
## 🚀 Why PINQWEN‑3.5‑9B‑1M?
| | |
|---|---|
| 🪟 **1,000,000‑token context** | Feed it entire codebases, whole books, months of chat logs — *at once*. A YaRN‑extended window on a gated‑linear‑attention hybrid backbone built to make long context **fast**, not just possible. |
| 🔓 **Uncensored** | De‑risked to answer directly, without the reflexive refusals of over‑aligned models. **You** decide what it works on. |
| 🧠 **Reasons in the open** | Native `…` chain‑of‑thought distilled from a panel of frontier teachers — transparent, inspectable reasoning you can actually read. |
| 💻 **Codes and reasons** | Tuned for software engineering, step‑by‑step problem solving, and precise technical explanation. |
| 👁 **Sees** | A full vision encoder — text **and** images in one 9B model. |
| ⚡ **Runs anywhere** | BF16, NVFP4 for Blackwell, and a complete GGUF quant ladder with the MTP speculative‑decode head. |
## 📦 This repository — BF16
Full‑precision (bfloat16) weights: the reference model — ideal for fine‑tuning, evaluation, vision‑language use, and as the source for further quantization. Keeps the vision encoder and the multi‑token‑prediction head. **Full 1M context.**
## ⚡ Quickstart
```python
from transformers import AutoModelForImageTextToText, AutoProcessor
model = AutoModelForImageTextToText.from_pretrained(
"Blackfrost-AI/PINQWEN-3.5-9B-1M-BF16", dtype="bfloat16", device_map="auto")
proc = AutoProcessor.from_pretrained("Blackfrost-AI/PINQWEN-3.5-9B-1M-BF16")
messages = [{"role": "user", "content": "Write a Python LRU cache with O(1) get/put and explain the design."}]
ids = proc.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(ids, max_new_tokens=1024)
print(proc.decode(out[0][ids.shape[1]:], skip_special_tokens=True))
```
## 🎛 Formats — every one is 1M context
| Format | Repository | Best for |
|---|---|---|
| **BF16** | [`PINQWEN-3.5-9B-1M-BF16`](https://huggingface.co/Blackfrost-AI/PINQWEN-3.5-9B-1M-BF16) | Reference precision, fine‑tuning, vision |
| **NVFP4** | [`PINQWEN-3.5-9B-1M-NVFP4`](https://huggingface.co/Blackfrost-AI/PINQWEN-3.5-9B-1M-NVFP4) | Fast serving on NVIDIA Blackwell |
| **GGUF** | [`PINQWEN-3.5-9B-1M-GGUF`](https://huggingface.co/Blackfrost-AI/PINQWEN-3.5-9B-1M-GGUF) | llama.cpp / local — full quant ladder + MTP |
## 🎯 Intended use
General reasoning, coding assistance, technical Q&A, ultra‑long‑document and whole‑codebase understanding, and multimodal (image + text) work. As an uncensored model, it will follow instructions directly — use responsibly and verify outputs for high‑stakes tasks.
## 📄 License
**Apache‑2.0.** Base architecture: Qwen 3.5 9B.
---
PINQWEN‑3.5‑9B‑1M — part of Blackfrost AI's Void model family. 🖤