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Blackfrost-AI
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
Blackfrost-AI
Model Tree
Qwen/Qwen3.5-9B
Input Modalities
Output Modalities
Supported Functionality
| 🪟 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 <think>…</think> 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. |
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.
python
from transformers import AutoModelForImageTextToText, AutoProcessormodel = 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))
| Format | Repository | Best for |
|---|---|---|
| BF16 | PINQWEN-3.5-9B-1M-BF16 | Reference precision, fine‑tuning, vision |
| NVFP4 | PINQWEN-3.5-9B-1M-NVFP4 | Fast serving on NVIDIA Blackwell |
| GGUF | PINQWEN-3.5-9B-1M-GGUF |
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.
Apache‑2.0. Base architecture: Qwen 3.5 9B.
| llama.cpp / local — full quant ladder + MTP |