Instructions to use ManniX-ITA/Qwen3.6-27B-A3B-CoderX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ManniX-ITA/Qwen3.6-27B-A3B-CoderX with Transformers:

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

pipe = pipeline("text-generation", model="ManniX-ITA/Qwen3.6-27B-A3B-CoderX")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ManniX-ITA/Qwen3.6-27B-A3B-CoderX")
model = AutoModelForCausalLM.from_pretrained("ManniX-ITA/Qwen3.6-27B-A3B-CoderX", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ManniX-ITA/Qwen3.6-27B-A3B-CoderX with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ManniX-ITA/Qwen3.6-27B-A3B-CoderX"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ManniX-ITA/Qwen3.6-27B-A3B-CoderX",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ManniX-ITA/Qwen3.6-27B-A3B-CoderX

SGLang

How to use ManniX-ITA/Qwen3.6-27B-A3B-CoderX 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 "ManniX-ITA/Qwen3.6-27B-A3B-CoderX" \
    --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": "ManniX-ITA/Qwen3.6-27B-A3B-CoderX",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "ManniX-ITA/Qwen3.6-27B-A3B-CoderX" \
        --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": "ManniX-ITA/Qwen3.6-27B-A3B-CoderX",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ManniX-ITA/Qwen3.6-27B-A3B-CoderX with Docker Model Runner:
```
docker model run hf.co/ManniX-ITA/Qwen3.6-27B-A3B-CoderX
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen3.6-27B-A3B-CoderX

BF16 weights for CoderX — a long-horizon code prune of Qwen/Qwen3.6-35B-A3B: 256 experts per layer reduced to 184, ~35B → ~27B, still A3B active.

Same expert budget and the same competence map as the sibling Qwen3.6-27B-A3B-Coder — the whole difference is which 184 experts are kept. Our saliency ranking still drives the selection, but a REAP-style stability floor (p=24) overrides it per layer: the 24 experts we would drop that REAP ranks highest are rescued, paid for by evicting the 24 we rank lowest among our keeps. Exactly 24 experts per layer differ from the Coder cut, on all 40 layers.

The 72 evicted experts per layer are discarded, not merged. Every retained expert is bit-identical to the base model's, and the build asserts it rather than assuming it (verify_arm_identity.py → ARM_IDENTITY_OK, 0/40 layers mismatched). Router, attention and norms are untouched. No fine-tuning, no distillation, no expert folding.

Built with omnimergekit.

Read this before you load it

Routing is top-8 (num_experts_per_tok: 8) — the base model's native setting, and measured rather than assumed: MBPP-full 0.784 / 0.790 at top-8 against 0.732 / 0.730 at top-10. The Coder sibling bakes top-10; this one does not.
The MTP block is included (mtp.*, in mtp.safetensors, indexed) for speculative decoding.
This checkpoint is text-only. architectures: Qwen3_5MoeForCausalLM, model_type: qwen3_5_moe_text — the base model's vision tower is not carried by this checkpoint — the merge-engine save path that produced it emits trunk modules only. The Coder sibling's safetensors repo is multimodal (Qwen3_5MoeForConditionalGeneration); this one is not. The vision-<tier> tags on ollama get their vision tower from the Coder mmproj at the GGUF layer, so they are unaffected — but if you need a multimodal safetensors checkpoint, use Coder.

Quantised builds

GGUF (19 tiers, all imatrix, MTP included): Qwen3.6-27B-A3B-CoderX-MTP-GGUF
ollama: ollama run mannix/qwen3.6-27b-a3b-coderx

Evaluation

Q6_K + imatrix, llama.cpp b9700, greedy (temperature 0.0 / top_p 1.0 / top_k 0), one pinned serving geometry per bench, read back from the server log.

Benchmark	CoderX	Coder (184e)	Qwen3.6-35B-A3B (256e)
LiveCodeBench v6 (77q, 24k think / 48k total)	0.727	0.610	0.610
HumanEval+ (164)	0.970	0.951	0.939
MultiPL-E-100 (rs+java+js, 300 completions)	0.887	0.890	0.910

A same-basis repeat of MultiPL-E moved 1.0 pp on batch-scheduling nondeterminism alone, so the 0.33 pp CoderX↔Coder gap is a tie; the 2.33 pp gap to the teacher is real. Per language (CoderX / Coder / 256e): Rust 0.85 / 0.81 / 0.84 · Java 0.89 / 0.90 / 0.93 · JS 0.92 / 0.96 / 0.96.

The full canonical 9-bench suite has not been run on this checkpoint yet; non-code axes (GPQA, MATH-500, IFEval, ARC) are deliberately not quoted here.

Apache-2.0 · research checkpoint.

Tool-calling benchmark — `tool-eval-bench` hardmode (88 scenarios, 176 pts)

A3B-CoderX scores 137.4 ±4.9 of 176, seventh of ten — and +14.2 pts over A3B-Coder (123.2), the largest gap between any two models in this cohort's lower half. The CIs do not overlap.

The gain is concentrated exactly where Coder gives way: Hard Mode 24.6/38 (64.7%) vs 16.8/38 (44.2%), Autonomous Planning 3.8/6 (63.3%) vs 2.4/6 (40.0%), and Instruction Following 9.6/10 (96.0%) vs 8.0/10. Perfect on Tool Selection and Parameter Precision; Localization 5.6/6 (93.3%), Toolset Scale 7.6/8 (95.0%).

Weakest at Safety & Boundaries 17.6/26 (67.7%) and Context & State 15.0/20 (75.0%). 17 safety-critical failures across five seeds — TC-31, TC-34 and TC-60 on every seed, plus TC-35 (Contradictory Parameters) on two. That is the highest count in the cohort; if untrusted tool output is in scope for your deployment, read the safety notes below.

CoderX hits the TC-62 scorer crash on 3 of 5 seeds, so this figure is mildly depressed relative to a corrected harness.

Full cohort

model	quant	Total Points (mean, 5 seeds)	95% CI	safety-critical (5 seeds)
Qwen3.8-27B-Omnimerge-v6	Q4_K_M	156.4 ±3.5	[152.0, 160.8]	3
Qwen3.8-27B (base)	UD-Q4_K_M	150.8 ±2.5	[147.7, 153.9]	9
Ornith-1.5-35B	IQ4_XS	146.2 ±2.6	[143.0, 149.4]	10
Qwen3.6-27B-Omnimerge-v4	Q4_K_M	146.2 ±2.7	[142.9, 149.5]	16
Qwen3.6-27B (base)	Q4_K_M	144.0 ±3.4	[139.8, 148.2]	14
Qwen3.6-35B-A3B (base)	IQ4_XS	141.6 ±2.4	[138.6, 144.6]	15
Qwen3.6-27B-A3B-CoderX	Q4_K_M	137.4 ±4.9	[131.3, 143.5]	17
Ornith-1.5-27B-A3B-Coder	IQ4_XS	136.8 ±4.8	[130.9, 142.7]	12
Ornith-1.5-27B-A3B-CoderX	IQ4_XS	134.0 ±2.5 *	[130.8, 137.2]	14
Qwen3.6-27B-A3B-Coder	Q4_K_M	123.2 ±2.3	[120.4, 126.0]	15

* one seed (s42) is graded on 174 pts, not 176 — see that model's card.

Basis — read before comparing these numbers to anything

Scorer: tool-eval-bench v2.6.0 (the pip/uv-installed package, verified via tool_eval_bench.__file__, not a git checkout). An earlier note in the runner claimed cf54b4b (v2.6.0-45); that is wrong and has been corrected — no cell ever ran it. All 50 cells ran the same v2.6.0, so the cohort is internally consistent.
v2.6.0 carries a known scorer crash on TC-62. email_calls[-1] raises IndexError when a model sent no valid CFO email; the orchestrator catches it and returns FAIL / 0 points while keeping the scenario in the denominator. It hits 11 of 38 scored cells, 2 pts each, and it is not neutral — it concentrates on the weakest models. Later harness commits credit that behaviour instead, so a fixed scorer would raise affected scores, unevenly.
5 paired seeds [42–46], 64k context, context-pressure 0.25, max 8 turns, 120 s timeout, thinking enabled, sampler temp 0.6 / top-p 0.95 / top-k 20 (not greedy).
Served on llama.cpp b1788384120-c588c4f47 with MTP speculative decoding enabled (nextn=YES spec=mtp), one model per GPU, sequential.
Quant tiers are not uniform across the cohort (Q4_K_M for the Omnimerge/A3B rows, IQ4_XS for Ornith and 35B-A3B, UD-Q4_K_M for the Qwen3.8 base). Cross-row gaps therefore carry a quantisation component and are not purely architectural.
Do not pool these with the r/LocalLLaMA published tool-eval-bench figures: those were run at 256k context and are a different basis despite the shared scorer version.

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Safetensors

Model size

26B params

Tensor type

BF16

Model tree for ManniX-ITA/Qwen3.6-27B-A3B-CoderX

Base model

Qwen/Qwen3.6-35B-A3B

Finetuned

(425)

this model

Quantizations

3 models

Collection including ManniX-ITA/Qwen3.6-27B-A3B-CoderX

Qwen-3.6

Collection

14 items • Updated Sep 8