Unrestricted Uncensored Qwen3.5‑9B Abliterated: Full Guide

What Is Qwen3.5‑9B “Abliterated” Variant?

Qwen3.5‑9B is Alibaba Cloud’s latest 9‑billion‑parameter multimodal foundation model, designed to handle text, images and even video with a single unified architecture.

It uses a hybrid design that combines Gated Delta Networks (a fast linear‑attention style block) with gated attention and sparse Mixture‑of‑Experts to deliver strong reasoning and coding performance at relatively low inference cost.

The base Qwen3.5‑9B model offers:

• 9B dense parameters across 32 layers with a 4096 hidden dimension and grouped‑query attention.​
• A native context window of 262,144 tokens, extendable toward 1M tokens depending on serving stack.
• Native multimodal support and coverage for around 201 languages and dialects, making it highly attractive for global use cases.

The “Qwen3.5‑9B Abliterated” collection is a community release that takes these open weights and removes almost all safety refusals, providing a fully uncensored, “0% refusal rate” variant in multiple formats (Safetensors, GGUF for text+vision, and MLX for Apple devices).

In tests reported by the author, the uncensored aggressive GGUF variant produced zero refusals across 465 adversarial prompts while preserving core capabilities, making it appealing for research, red‑teaming and offline experimentation where safety filtering is controlled by the user rather than the model.

Because Qwen3.5‑9B is released under an Apache 2.0‑style license and Abliterated is packaged as community weight conversions, the model can be self‑hosted with no per‑token license fee, subject only to infrastructure cost and any local policy or compliance constraints.

Key Specs and Capabilities

Architecture and Core Features

According to Alibaba and third‑party summaries, Qwen3.5‑9B has the following notable properties:

• Parameters: 9B dense (no MoE routing at inference time for the 9B variant).
• Layers: 32 transformer blocks using an 8×(3×DeltaNet → FFN → 1×Attention → FFN) pattern.​
• Attention: Grouped‑Query Attention with 16 attention heads and 4 KV heads.​
• Hidden size: 4096 with SwigLU activations and RMS normalization.​
• Context window: 262k tokens natively, with some deployments reporting extension up to around 1M tokens using long‑context techniques.
• Multimodality: Unified vision‑language foundation trained with multimodal tokens from the start, not bolted on later.
• Languages: Around 201 languages and dialects covered, with strong multilingual benchmarks.

For local users, this translates into a compact yet very capable model that can run on consumer‑grade GPUs or high‑end CPUs when quantized to 4‑bit GGUF.

Performance and Benchmarks

Official and third‑party testing place Qwen3.5‑9B among the strongest sub‑10B models on reasoning, coding and multimodal tasks.

A technical spec sheet summarising Alibaba’s internal results reports that Qwen3.5‑9B achieves roughly the following on key academic benchmarks:

• MMLU‑Pro (general knowledge): ≈82.5%.
• GPQA Diamond (graduate‑level reasoning): ≈81.7%.
• Multilingual benchmarks like MMMLU and INCLUDE: scores competitive with or above many much larger frontier models.
• Multimodal reasoning (MMMU‑Pro): around 69–70%, clearly ahead of many previous‑generation small vision‑language models.

One independent review compared Qwen3.5‑9B against a 120B‑parameter open model (“GPT‑OSS‑120B”) and reported that Qwen3.5‑9B:

• Outperformed the 120B model on MMLU‑Pro (82.5 vs 80.8) and GPQA Diamond (81.7 vs 80.1).​
• Achieved stronger multilingual scores on MMMLU (81.2 vs 78.2).​
• Scored around 70.1 on MMMU‑Pro vision reasoning, roughly 22.5% higher than a compact GPT‑5‑Nano competitor.​

Artificial Analysis, which tracks an “Intelligence Index” for many models, rates Qwen3.5‑9B (reasoning variant) at 32 points, making it the most intelligent model under 10B parameters and substantially ahead of peers like Falcon‑H1R‑7B (16) and NVIDIA Nemotron Nano 9B V2 (15).​

Hardware Requirements and Quantization

Running Qwen3.5‑9B unquantized generally requires around 18GB of RAM or VRAM, but most local setups use quantized GGUF or Q‑formats. A practical guide to local deployment recommends the following for GGUF variants:

• Q4_K_M (~5GB): Best for laptops and small servers; minimal quality loss with big memory savings.
• Q5_K_M (~6.5GB): Slightly higher quality at modest extra memory.
• Q8_0 (~9GB): Near‑FP16 quality; needs more RAM but still feasible on consumer GPUs.

Reviewers running Qwen3.5‑9B Q4 quantizations report that a 16GB RAM laptop or Apple Silicon Mac is sufficient, with no dedicated GPU strictly required, and speeds around 30–60 tokens per second depending on hardware and configuration.

The Abliterated collection exposes Qwen3.5‑9B in multiple formats (Safetensors for PyTorch/Transformers, GGUF for llama.cpp‑based stacks, and MLX for Apple devices), so users can pick the runtime that best matches their hardware.

USP of Qwen3.5‑9B Abliterated

The unique selling points of the Abliterated variant come from combining Qwen3.5‑9B’s strong base model with community‑driven modifications tailored for unrestricted local use:

1. Fully uncensored behaviour: The collection is explicitly advertised as “fully uncensored Qwen3.5‑9B — 0% refusal rate”, and the GGUF “aggressive” build reports zero refusals across 465 adversarial prompts in testing.
2. High intelligence at small size: With an Intelligence Index of 32, Qwen3.5‑9B delivers performance comparable to, or exceeding, some 100B+‑parameter models while remaining runnable on a single consumer GPU or 16GB RAM machine.
3. Native multimodality: Unlike many 7B–9B models that are text‑only or have add‑on vision adapters, Qwen3.5‑9B is natively multimodal and trained with images and video from the ground up.
4. Extremely long context: A 262k native context window (extendable with advanced serving) enables whole‑repository code analysis, long document workflows and multi‑turn agent chains without frequent truncation.
5. Friendly licensing and open tooling: Apache‑style licensing, open weights and broad support across Ollama, llama.cpp, vLLM and LM Studio give developers maximum flexibility for commercial or on‑prem use.

For users specifically looking for an uncensored, high‑IQ, multimodal model that still fits on a single workstation, Qwen3.5‑9B Abliterated is currently one of the most compelling options.

Quick Model Comparison (Qwen3.5‑9B vs Key Competitors)

The table below summarises how Qwen3.5‑9B stacks up against a few representative competitors in the same rough size / use‑case class.

While exact scores differ across benchmarks and providers, multiple independent analyses converge on the conclusion that Qwen3.5‑9B is currently best‑in‑class among open models under 10B parameters, especially when multimodality and long context are required.

Installation: How to Run Qwen3.5‑9B Abliterated

Qwen3.5‑9B Abliterated can be run through several popular stacks:

• Ollama (fastest way to get base Qwen3.5‑9B running, then swap weights if desired).
• llama.cpp (and front‑ends built on it, e.g. LM Studio, KoboldCPP, TensorBlock, etc.).
• vLLM or other Python servers for high‑throughput API serving.

Below is a practical, non‑platform‑specific walkthrough, followed by more detailed OS‑specific notes.

Step 1: Check Hardware and Choose Quantization

Before downloading anything, confirm your machine:

• Has at least 8GB RAM (16GB strongly recommended).
• Has at least 10GB of free disk space for model files and tooling.​

Next, choose a quantization level based on your hardware:

• Laptop or low‑VRAM GPU (≤8GB): Q4_K_M GGUF (~5GB) – best trade‑off of speed and quality.​
• Mid‑range GPU (10–16GB VRAM): Q5_K_M (~6.5GB) – higher quality while still efficient.​
• High‑end GPU (≥16GB VRAM): Q8_0 (~9GB) or even BF16 (~17GB) if you want near‑lossless quality.

The Abliterated collection provides GGUF quantizations similar to these, so match the file size and quant type to your machine.

Step 2: Fastest Route – Ollama (Base Qwen3.5‑9B)

Ollama is often the quickest way to get a working Qwen3.5‑9B chat running on Mac, Windows or Linux.

1. Install Ollama from the official website or via Homebrew on macOS (brew install ollama).​
2. Start the Ollama service (done automatically on most systems).
3. In a terminal, run:bashollama run qwen3.5:9b
   This command downloads a pre‑built Q4_K_M quantized version (~6.6GB) and starts an interactive chat session.​
4. You can now talk to Qwen3.5‑9B via the terminal, or call it over an OpenAI‑compatible HTTP API at http://localhost:11434 from your applications.

To use the Abliterated weights instead of the default safe variant, you have two main options:

• Use a llama.cpp‑based runtime (see below) and directly load the Abliterated GGUF.
• Or build a custom Ollama Modelfile that points to the Abliterated GGUF or Safetensors—Ollama’s docs describe how to reference external weights in a custom model; the process is similar to other community GGUF models.

Step 3: Running Abliterated via llama.cpp / LM Studio

If you want full control and guaranteed use of the uncensored Abliterated quant, llama.cpp is the most direct route. Many GUIs—including LM Studio—are wrappers around llama.cpp‑style backends.

Basic workflow with llama.cpp (command‑line):​

1. Install build tools and clone llama.cpp from GitHub.
2. Build with your desired backend:
   • NVIDIA GPU: cmake -B build -DLLAMA_CUDA=ON && cmake --build build.
   • Apple Silicon: cmake -B build -DLLAMA_METAL=ON && cmake --build build.​
3. Download the Qwen3.5‑9B Abliterated GGUF (for example, qwen3.5-9b-abliterated-q4_k_m.gguf) from the Hugging Face collection.
4. Launch an API server:bash./build/bin/llama-server \
-m qwen3.5-9b-abliterated-q4_k_m.gguf \
-c 8192 \
--port 8080
   
   • -c 8192 sets an 8k context for normal chat; increase for document tasks.​
5. Connect to http://localhost:8080 from your app or a simple HTTP client.

LM Studio follows a similar flow but with a GUI:​

• Open LM Studio, search for qwen3.5-9b.
• Choose a suitable quant (e.g. Q4_K_M GGUF Abliterated build if indexed, or manually add the GGUF file).
• Click “Download” and then “Run”, adjusting context length and batch size in the UI.

Step 4: High‑Throughput Serving with vLLM

For back‑end servers handling many concurrent users, vLLM provides high‑throughput inference with techniques like PagedAttention and continuous batching.​

A common pattern is:

1. Install vLLM:bashpip install vllm
2. Run the server using the original Hugging Face Qwen3.5‑9B repo or a local checkpoint path:bashvllm serve Qwen/Qwen3.5-9B --max-model-len 8192
3. Then, replace the checkpoint with an Abliterated Safetensors replica or fine‑tuned copy if you require uncensored behaviour.

vLLM is more often used with full‑precision or GPTQ/awq quantizations than GGUF, so this path is typically chosen for servers with ample GPU memory.

OS‑Specific Notes

A step‑by‑step deployment guide for Qwen3.5‑9B highlights a few extra OS details:​

• macOS (Apple Silicon):
  • Install Xcode Command Line Tools (xcode-select --install).
  • Build llama.cpp with -DLLAMA_METAL=ON to enable Metal GPU acceleration.
  • A Mac mini M4 with 16GB unified memory can achieve around 40–60 tokens/s with Q4 quantization.​
• Windows:
  • Either use WSL2 (Ubuntu) or native Windows builds.
  • For WSL2, run wsl --install, then follow Linux instructions for Ollama or llama.cpp.
  • Ensure NVIDIA drivers and CUDA versions are compatible; mismatches are a common source of GPU errors.​
• Linux:
  • Install CUDA for NVIDIA GPUs; build llama.cpp with -DLLAMA_CUDA=ON.
  • For containerised deployments, use official Docker images for Ollama or llama.cpp and expose ports appropriately.​

How to Benchmark and Test Qwen3.5‑9B Abliterated

Because Abliterated mainly changes safety policies rather than core weights, its raw capabilities are close to the base Qwen3.5‑9B model. Still, local benchmarking is important to verify speed, quality and refusal behaviour on your hardware and prompts.

1. Measure Speed: Tokens per Second and Latency

A local deployment guide recommends using built‑in tools such as llama-bench for llama.cpp to measure:​

• Tokens per second (TPS): Throughput during generation.
• First‑token latency: How long before the first output token appears.

To benchmark speed:

1. Run llama-bench with your Abliterated GGUF quant to get TPS for various context lengths and batch sizes.
2. Record results for different quantizations (Q4 vs Q5 vs Q8) and hardware configurations (CPU‑only vs GPU‑accelerated).
3. Use these numbers to choose a default quant and context window that match your latency budget.

Anecdotal reports for Qwen3.5‑9B Q4 on modern consumer hardware suggest TPS in the 30–60 range for typical 4k–8k context chats, which feels responsive for interactive use.

2. Evaluate Quality: Reasoning, Coding, and Hallucination

Artificial Analysis provides a useful macro view: Qwen3.5‑9B (reasoning) leads all sub‑10B models in its Intelligence Index but also exhibits relatively high hallucination (around 82%) and modest answer accuracy (~14.7%) on a hallucination benchmark they track.​

To test quality locally:

• Prepare a small, representative evaluation set: math word problems, multi‑step reasoning questions, coding tasks, and domain‑specific prompts.
• Compare Abliterated’s answers against ground truth or known‑good outputs from a trusted reference model.
• Log failure modes (hallucinations, mis‑reasoning, unsafe outputs) and consider adding an external moderation or verification layer rather than relying on model self‑censorship.

3. Probe Uncensored Behaviour and Safety

The Abliterated collection and uncensored GGUF thread explicitly emphasise that this variant answers essentially all prompts, with 0% refusal across hundreds of tests, occasionally adding brief disclaimers but never hard refusals.

If you are using the model for research or red‑teaming, recommended tests include:

• Refusal check: Feed a range of safety‑sensitive prompts and verify that the model no longer returns stock “I can’t help with that” refusals.
• Prompt‑steering check: Confirm that system prompts and role instructions can still shape tone and style despite the relaxed safety rules.
• Content‑filter integration: Pair the model with external classifiers, rule‑based filters or human review workflows, especially if deploying in end‑user‑facing applications.

Because Qwen3.5‑9B Abliterated is intentionally unrestricted, real‑world deployments should assume full responsibility for safety, legality and compliance in their jurisdiction.

4. Compare Against Baselines

To understand whether Abliterated is the right fit, compare it in your own environment with:

• Base Qwen3.5‑9B via Ollama (for a safety‑filtered baseline).​
• A strong but smaller model (e.g. Qwen3.5‑4B or Qwen2.5‑7B) for resource comparison.
• A competitor such as Gemma‑2‑9B or Nemotron Nano 9B on a fixed prompt set.

Key metrics to track:

• Accuracy on your task‑specific evaluation set.
• Average completion length and verbosity.
• Refusal rate (how often the model declines to answer).
• Average and p95 latency.

Pricing and Cost Considerations

There is no direct per‑token fee for running Qwen3.5‑9B or its Abliterated variant locally. Instead, costs break down into:

• One‑time hardware cost: A 16GB RAM laptop or mid‑range GPU is sufficient for Q4 quantisation; higher‑end GPUs support FP16 or large batch sizes.
• Electricity and maintenance: Ongoing but relatively small for a single workstation.
• Opportunity cost vs cloud APIs: For low‑volume workloads, a pay‑per‑use cloud LLM may still be cheaper; for continuous use or many users, local Qwen3.5‑9B becomes cost‑effective.

Some benchmarking sites note that Qwen3.5‑9B is not yet widely offered as a managed API with public pricing, framing it primarily as an open‑weights model intended for self‑hosting and on‑prem deployments. Other Qwen family models (such as Qwen2.5 Turbo) are available through commercial providers, but analyses suggest those API offerings can be more expensive per token than some competing non‑reasoning models.

For businesses, a useful strategy is:

• Start with local Qwen3.5‑9B Abliterated for prototyping, evaluation, and privacy‑sensitive workloads.
• Use cloud models only where extra quality or reliability justifies the additional per‑token cost.

Example Use Cases and Demos

Qwen3.5‑9B Abliterated is especially suited for:

• Agentic coding assistants: Sub‑10B reasoning models are already powering local code assistants; Reddit discussions ask whether Qwen3.5‑9B is “enough” for multi‑tool agentic coding workflows, with many users reporting positive experiences.
• Long‑context document analysis: The 262k context window allows full contract bundles, technical documentation sets, or software repositories to be ingested and queried in a single session.
• Multilingual and cross‑cultural apps: With coverage of 201 languages and strong multilingual benchmarks, Qwen3.5‑9B can underpin chatbots and knowledge bases serving global audiences.
• Vision‑heavy applications: Its strong MMMU‑Pro performance makes it suitable for document understanding, chart/question answering, and general visual QA tasks.​

For a simple local “demo stack”, you can:

1. Run Abliterated Q4_K_M GGUF in llama.cpp.
2. Expose an HTTP endpoint via llama-server.
3. Build a small web UI (e.g. a React or Python Flask front‑end) that supports:
   • Text chat.
   • Image upload for captioning and Q&A.
   • A toggle between “safe baseline” (e.g. cloud API) and “Abliterated local” for side‑by‑side comparison.

This type of demo clearly shows stakeholders the trade‑offs between unrestricted local models and managed cloud offerings.

How Qwen3.5‑9B Abliterated Differs from Other Variants

Within the Qwen3.5 family and adjacent releases, there are several variants worth distinguishing:

• Base vs reasoning vs Turbo: Some Qwen3.5 models are tuned for fast throughput (Turbo), others for deep chain‑of‑thought reasoning. The 9B reasoning variant emphasises multi‑step thinking and agentic behaviour at the cost of more tokens per answer.​
• Safe vs uncensored: Official releases generally retain safety training and will refuse certain prompts; Abliterated removes most refusal behaviour, shifting safety control to the application layer.
• Size tiers: 0.8B, 2B, 4B and 9B small models target mobile, edge, laptop and workstation tiers respectively, with the 4B and 9B delivering performance close to—or exceeding—previous 80B models in some tasks.

Compared with these, Qwen3.5‑9B Abliterated sits at the top of the small‑model range, combining:

• Maximum capability among sub‑10B models.
• Unrestricted output.
• Multimodal and long‑context features that smaller siblings cannot fully match.

Practical Tips for Getting the Best Results

Based on community recommendations and vendor defaults, a few practical tuning tips include:

• Sampling settings: Start with temperature ≈1.0, top_p ≈ 0.95, top_k ≈ 20, and a modest presence penalty (around 1.5) for creative chat; these resemble defaults used in Ollama’s Qwen3.5 preset.​
• Thinking vs non‑thinking mode: Qwen3.5 supports explicit “thinking” (chain‑of‑thought) modes; the uncensored GGUF author suggests slightly different sampling settings depending on whether you want verbose reasoning or concise direct answers.​
• Context length: Do not always allocate the full 262k context; start with 4k–8k for chat and go higher only when needed, to save memory and improve speed.​
• Threading and batch size: Match thread count to physical CPU cores and gradually increase batch size on the GPU side until you see diminishing returns or OOM errors.​

Frequently Asked Questions (FAQ)

1. Is Qwen3.5‑9B Abliterated free to use?

Yes. The underlying Qwen3.5‑9B weights are released under an Apache‑style open‑source license, and the Abliterated conversions are community packages, so you only pay for hardware and electricity.

2. Can I run it on a laptop without a GPU?

Yes, with a Q4 GGUF quantization a modern 16GB RAM laptop (or Apple Silicon Mac) can run Qwen3.5‑9B at usable speeds, although GPU acceleration provides 2–5× faster inference.

3. How is Abliterated different from the normal Qwen3.5‑9B?

Abliterated removes almost all safety refusals, delivering a fully uncensored, “0% refusal” experience while keeping the core model architecture and capabilities of Qwen3.5‑9B.

4. Is Qwen3.5‑9B better than Gemma‑2‑9B or Nemotron Nano 9B?

Benchmarks from independent analysts suggest Qwen3.5‑9B leads both Gemma‑2‑9B and Nemotron Nano 9B on reasoning and multimodal tasks, with about double the Intelligence Index of the latter models.

5. Is it safe to use an uncensored model in production?

By design, Abliterated does not refuse harmful or sensitive prompts, so safety must be enforced at the application layer using filters, policies and human review to meet legal and ethical requirements.