Instructions to use keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF", device_map="auto")

llama-cpp-python

How to use keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF",
	filename="Qwen3.6-35B-A3B-REAM-192-heretic-APEX-Compact-Q3_K_L.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF:Q3_K_L
# Run inference directly in the terminal:
llama cli -hf keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF:Q3_K_L

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF:Q3_K_L
# Run inference directly in the terminal:
llama cli -hf keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF:Q3_K_L

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF:Q3_K_L
# Run inference directly in the terminal:
./llama-cli -hf keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF:Q3_K_L

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF:Q3_K_L
# Run inference directly in the terminal:
./build/bin/llama-cli -hf keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF:Q3_K_L

Use Docker

docker model run hf.co/keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF:Q3_K_L

LM Studio
Jan

vLLM

How to use keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF:Q3_K_L

SGLang

How to use keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Ollama
How to use keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF with Ollama:
```
ollama run hf.co/keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF:Q3_K_L
```

Unsloth Studio

How to use keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF to start chatting

How to use keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF:Q3_K_L

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF:Q3_K_L"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF:Q3_K_L

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF:Q3_K_L

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF:Q3_K_L

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF:Q3_K_L" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF with Docker Model Runner:
```
docker model run hf.co/keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF:Q3_K_L
```

Lemonade

How to use keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF:Q3_K_L

Run and chat with the model

lemonade run user.Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF-Q3_K_L

List all available models

lemonade list

Qwen3.6-35B-A3B-REAM-192-heretic — APEX-GGUF

⚠️ Preliminary release. Quantized from the bf16 GGUF master of keithnull/Qwen3.6-35B-A3B-REAM-192-heretic. The only validation performed so far is (a) Heretic's first-token KL + refusal-marker eval against its 100-prompt evaluation split during the optimization run, (b) a five-prompt qualitative chat spot-check on the bf16 weights, and (c) basic generation-coherence smoke tests on the resulting GGUFs. No perplexity, KL-divergence, or academic-benchmark numbers have been measured against this APEX recipe yet — the published APEX results are on the un-abliterated Qwen3.5-35B-A3B reference, not this model. Treat this release as a preview while those evaluations are pending. Card will be updated with hard numbers as they land.

APEX (Adaptive Precision for EXpert Models) MoE-aware mixed-precision quantizations of keithnull/Qwen3.6-35B-A3B-REAM-192-heretic. For standard llama.cpp K-quants of the same model, see keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-GGUF.

What is APEX?

https://github.com/mudler/apex-quant

Available quantizations

File	Size	BPW	Notes
`Qwen3.6-35B-A3B-REAM-192-heretic-APEX-IQuality-Q5_K_M.gguf`	17 GB	5.30	Highest quality, smallest "premium" tier. Q6_K (edge) / Q5_K (near) / IQ4_XS (middle) routed experts + Q8_0 shared expert + Q6_K attention + diverse-imatrix calibration. APEX claims F16-equivalent perplexity on the reference model.
`Qwen3.6-35B-A3B-REAM-192-heretic-APEX-IBalanced-Q5_K_M.gguf`	19 GB	5.91	Highest quality with pure K-quant inference. Q6_K (edge) / Q5_K (middle) routed experts + Q8_0 shared expert + Q6_K attention + diverse-imatrix calibration. Slightly larger than I-Quality but no IQ-format decode overhead.
`Qwen3.6-35B-A3B-REAM-192-heretic-APEX-ICompact-Q3_K_L.gguf`	~13 GB	~4.02	Same size class as standard Q4_K_M, recommended daily driver. Q4_K (edge) + Q3_K (middle) routed experts + Q6_K shared + Q4_K attention + imatrix scales. APEX claims this beats Q4_K_M-class quants on perplexity AND HellaSwag at the same footprint.
`Qwen3.6-35B-A3B-REAM-192-heretic-APEX-Compact-Q3_K_L.gguf`	13 GB	4.02	Same tensor recipe as I-Compact but without imatrix calibration. Useful as a "no-imatrix" baseline reference.
`Qwen3.6-35B-A3B-REAM-192-heretic-APEX-Mini-IQ2_S.gguf`	12 GB	~3.80	Smallest viable. Q3_K (edge) / IQ2_S (middle) expert gradient + imatrix. Fits 16 GB VRAM consumer GPUs.

The "I-" prefix in APEX nomenclature means "imatrix-calibrated" — it changes the scale values stored in K-quant blocks but does not change the tensor formats.

A note on filenames

APEX GGUFs are mixed-precision — every file contains multiple llama.cpp quant types simultaneously (e.g. Q6_K for edge layers, Q5_K for near-edge, IQ4_XS for middle-layer routed experts, Q8_0 for shared experts, etc). HuggingFace's hardware-compatibility widget and various model-listing surfaces auto-detect a file's quantization by scanning the filename for a recognized K-quant or IQ-quant token (Q4_K_M, Q3_K_S, IQ4_XS, etc). Because no single token is strictly correct for a mixed-precision file, the parser would otherwise show "Unknown" / "Cannot determine variant" and the model wouldn't surface on hardware-filtered model lists.

To work around this, each file in this repo has a parser-friendly K-quant suffix appended that matches its dominant middle-layer expert quantization (since middle layers comprise the bulk of routed-expert parameters and most strongly determine the file's effective BPW). For example: APEX I-Compact uses Q4_K-edge + Q3_K-middle experts, so its file is named ...-APEX-ICompact-Q3_K_L.gguf. The Q3_K_L tag isn't exactly the file's contents — the actual tensor types are mixed — but it's the closest single recognizable token for HF's parser, and the BPW lands close to Q3_K_L's 4.27.

Pay attention to the APEX tier name (Mini / Compact / I-Compact / I-Balanced / I-Quality) for the real description of what's inside each file. Treatt the "Q_#" suffix purely as a UX hint for HF's display layer.

Vision support

These GGUFs are text-only weights, but the underlying REAM-192 architecture preserves the Qwen3.6-VL vision tower. Pairing with the standard Qwen3.6-VL projector enables image input via llama-server's OpenAI-compatible chat-completions endpoint.

The compatible projector is mirrored here for convenience:

File	Size	Notes
`mmproj-REAP-26B-F16.gguf`	899 MB	F16 vision encoder + projector. Bit-identical mirror of the file in keithnull/Qwen3.6-VL-REAP-26B-A3B-GGUF, which was itself converted from the vision shard in atbender/Qwen3.6-VL-REAP-26B-A3B.

Why a REAP-derived projector works with REAM

Both REAM (this model) and REAP (atbender's source) preserve the unmodified Qwen3.6-VL vision encoder, hidden-state dimension, and tokenizer — neither method touches the vision tower or the input-embedding layer. The mmproj's image-token embeddings land in the same hidden-state space the LM was originally trained against. The APEX recipe further keeps shared experts and attention layers at high precision (Q6–Q8) across every tier, so vision-LM alignment survives even at the most aggressive (Mini-IQ2_S) routed-expert quantization.

This is a cross-recipe pairing without any vision-specific fine-tuning. A REAM-native projector would likely improve quality further, but is not yet released.

Validation (2026-05-12, n=1, qualitative)

Single-rater smoke test on a 590×904 JPEG product photo of a Canon PowerShot G5, running APEX Mini-IQ2_S + this projector on Apple M3 Metal:

Correctly identified the camera (Canon PowerShot G5) and attached flash (Canon Speedlite 420EX).
Read fine print: 5.0 MEGA PIXELS, CANON ZOOM LENS 7.2-28.8mm 1:2.0-3.0.
Noted the CENEO watermark across the middle of the image.
~40 s end-to-end via an agent harness (pi-mono) on the 10 GB Mini quant.

No quantitative VLM benchmarks (MMMU, MM-Vet, OCR-Bench, etc.) have been run against this pairing yet. The above is a smoke test, not a result. The same probe was previously validated against the REAP-26B-A3B Q4_K_M native pairing on 2026-05-01 — REAM Mini matches REAP-native on this image.

Usage

llama-server \
  -m Qwen3.6-35B-A3B-REAM-192-heretic-APEX-Mini-IQ2_S.gguf \
  --mmproj mmproj-REAP-26B-F16.gguf \
  -c 32768 -ngl 99 --jinja

Then POST OpenAI-compatible chat completions with an image_url content block (base64 data URI, data:image/jpeg;base64,…). Confirmed working on JPEG and PNG; WebP is rejected by llama-server's mtmd loader at the time of writing — convert to JPEG/PNG first.

For grounding-heavy tasks, the upstream Qwen-VL guidance applies: add --image-min-tokens 1024 if accuracy on text reading or bounding-box-style queries drops.

Refusal and capability metrics (inherited from source bf16)

Metric	This model (bf16 source)	Pre-heretic (REAM-192)
Refusals on Heretic eval set	✅ 10 / 100	❌ ~80 / 100
First-token KL divergence	0.0008	0 (by definition)

These numbers are from the Heretic optimization run on the bf16 weights, prior to APEX quantization. APEX-specific refusal/KL deltas (whether Q3-tier or IQ-tier middle layers change the refusal-suppression effectiveness) have not been measured. We do not expect significant change but it is on the validation TODO list.

Quantization recipe

Source: Qwen3.6-35B-A3B-REAM-192-heretic-bf16.gguf (51 GB / 16.01 BPW), the bf16 master from the standard GGUF repo.
Tooling: APEX configs + goblincore/llama-cpp-turboquant build of llama-quantize and llama-imatrix.
Imatrix calibration corpus: bartowski's calibration_datav3.txt. APEX's own pipeline references a calibration_v1.2.txt that isn't bundled in the repo; we used the de-facto community standard instead. Quality difference is reportedly small per APEX's own ablations.
Imatrix generation: 32 chunks × 2048 ctx (corpus length capped the chunk count), all 40 transformer blocks GPU-resident on H100.
Profile-to-config mapping: APEX --profile compact / i-compact / i-balanced / i-quality / mini (the qwen36_heretic_*.txt configs are byte-identical to qwen36_35b_*.txt — no heretic-specific tuning beyond the layer-gradient strategy that's universal across the Qwen3.5/3.6 family).

Quick start

Inference (any APEX tier)

./llama-cli \
  -m Qwen3.6-35B-A3B-REAM-192-heretic-APEX-ICompact-Q3_K_L.gguf \
  -p "Write a Python function that returns the nth Fibonacci number." \
  -n 1000 --temp 0.7 --n-gpu-layers 999

(Use -n 1000 minimum since Qwen3.6's default thinking mode emits long [Start thinking] ... [End thinking] blocks before the answer. Pass /no_think in the prompt to skip thinking for quick sanity checks.)

Server

./llama-server \
  -m Qwen3.6-35B-A3B-REAM-192-heretic-APEX-IBalanced-Q5_K_M.gguf \
  --host 0.0.0.0 --port 8080 \
  --ctx-size 32768 \
  --n-gpu-layers 999

Preliminary local code-gen smoke test (ICompact only)

A small-scale single-run shakedown of the ICompact-Q3_K_L tier against a real coding workload, run via llama-server from TheTom-llama-cpp-turboquant with KV cache q8_0/turbo3, ngram-simple speculative decode, free-form thinking with --reasoning-budget 1500, and ctx 32768. Date: 2026-05-09.

This is not a benchmark. Sample sizes are tiny (2 standalone tasks + 3 agentic tasks), n=1 per task, single rater. It exists to confirm the recipe survives an agentic workload without obvious breakage, ahead of the larger evals on the validation TODO list.

2× standalone TypeScript code-gen (mulberry32 SeededRng + LRUCache<K,V>) | 33/33 vitest tests pass on the produced sources |
3× agentic tasks against a ~360-test repo
(vitest sanity / Box-Muller nextGaussian + stats test / multi-file readonly name field on post-processing Effect classes + tsc --noEmit) | 3/3 clean diffs; vitest pass; tsc --noEmit clean |
Pre-bench probes terminated cleanly (finish_reason=stop, no V-quant attention loop). On the nextGaussian task the agent produced library-grade Box-Muller — caching the second sample of each pair and patching fork() to copy the cache — without being prompted for either detail. The test (10 000 samples; assert |mean| < 0.1 and |stddev − 1| < 0.1) passed first try.

What this can support saying: the I-Compact recipe runs end-to-end through Read/Grep/Edit/Bash + self-verify loops and produces diffs that pass vitest and tsc on the cases tried.

What it cannot support saying: anything about quality vs other APEX tiers (not benched here), vs vanilla REAM Q4_K_M on agentic loops (no comparison run yet), or about academic-eval scores (still deferred).

Per-task diffs, raw stdout/stderr, and the produced source snapshots are uploaded under bench/2026-05-09-icompact-smoke/ in this repo.

Lineage

Qwen/Qwen3.6-35B-A3B  (base, 35.11B params, 256 routed experts)
        │
        ▼  REAM (Cerebras / Samsung SAIL Montreal expert merging)
        │  256→192 routed experts
        │
keithnull/Qwen3.6-35B-A3B-REAM-192  (27.05B params, vision tower preserved)
        │
        ▼  Heretic (directional ablation + Optuna kernel optimization)
        │  Custom fork goblincore/heretic@qwen3_5-packed-experts-v2
        │
keithnull/Qwen3.6-35B-A3B-REAM-192-heretic  (bf16 safetensors)
        │
        ▼  llama.cpp convert_hf_to_gguf.py
        │
keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-GGUF  (bf16 master + standard K-quants)
        │
        ▼  APEX mixed-precision recipe + imatrix calibration
        │
keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF  (this repo)

Credits

@mudler and the LocalAI team for APEX — the MoE-aware mixed-precision quantization recipe, configs, and pipeline that produced these GGUFs.
@bartowski1182 for the diverse calibration corpus used for imatrix generation.
@p-e-w for Heretic and the underlying abliteration framework.
@Sehyo for the per-trial fused-expert ablation approach in PR #207.
@grimjim for the Magnitude-Preserving Orthogonal Ablation formulation.
Inspired by @llmfan46's prior Qwen3.6 heretic work.

License

Apache 2.0, inherited from the source model.

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GGUF

Model size

27B params

Architecture

qwen35moe

Hardware compatibility

2-bit

3-bit

5-bit

Model tree for keithnull/Qwen3.6-35B-A3B-REAM-192-heretic-APEX-GGUF

Base model

Qwen/Qwen3.6-35B-A3B

Finetuned

keithnull/Qwen3.6-35B-A3B-REAM-192

Finetuned

keithnull/Qwen3.6-35B-A3B-REAM-192-heretic

Quantized

(2)

this model