Instructions to use mconcat/Qwopus3.6-27B-v2-AWQ-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mconcat/Qwopus3.6-27B-v2-AWQ-4bit with Transformers:

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

pipe = pipeline("text-generation", model="mconcat/Qwopus3.6-27B-v2-AWQ-4bit")
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 AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("mconcat/Qwopus3.6-27B-v2-AWQ-4bit")
model = AutoModelForImageTextToText.from_pretrained("mconcat/Qwopus3.6-27B-v2-AWQ-4bit")
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?"}
        ]
    },
]
inputs = processor.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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use mconcat/Qwopus3.6-27B-v2-AWQ-4bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mconcat/Qwopus3.6-27B-v2-AWQ-4bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mconcat/Qwopus3.6-27B-v2-AWQ-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mconcat/Qwopus3.6-27B-v2-AWQ-4bit

SGLang

How to use mconcat/Qwopus3.6-27B-v2-AWQ-4bit 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 "mconcat/Qwopus3.6-27B-v2-AWQ-4bit" \
    --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": "mconcat/Qwopus3.6-27B-v2-AWQ-4bit",
		"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 "mconcat/Qwopus3.6-27B-v2-AWQ-4bit" \
        --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": "mconcat/Qwopus3.6-27B-v2-AWQ-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mconcat/Qwopus3.6-27B-v2-AWQ-4bit with Docker Model Runner:
```
docker model run hf.co/mconcat/Qwopus3.6-27B-v2-AWQ-4bit
```

Qwopus3.6-27B-v2-AWQ-4bit

AutoAWQ-format INT4 (W4A16) quantization of Jackrong/Qwopus3.6-27B-v2, a Claude Opus reasoning-distilled fine-tune of Qwen 3.6 27B.

The hybrid DeltaNet + softmax attention architecture is preserved, the 1-layer MTP head is included for speculative decoding, and the multimodal processor metadata is kept intact. APEX-style edge protection keeps the first and last layers in BF16 for quality.

Quick start

Requires vLLM ≥ 0.21.0:

vllm serve mconcat/Qwopus3.6-27B-v2-AWQ-4bit \
  --tensor-parallel-size 1 \
  --max-model-len 16384 \
  --speculative-config '{"method": "mtp", "num_speculative_tokens": 3}' \
  --tool-call-parser qwen3_coder \
  --reasoning-parser qwen3 \
  --enable-auto-tool-choice \
  --trust-remote-code

Benchmarks

Evaluated with lm-evaluation-harness on a single NVIDIA B300 SXM6, 100 samples per task, 0-shot CoT, max_gen_toks=4096:

Task	Qwen 3.6 27B (base)	Qwopus 3.6 v2 (source BF16)	This (AWQ-4bit)
GSM8K (flexible-extract)	65.0%	87.0%	85.0%
ARC-Challenge (acc_norm)	46.0%	45.0%	47.0%
TruthfulQA-MC2	55.1%	59.3%	59.3%
IFEval (inst_level_strict)	40.5%	42.3%	42.9%

Quantization preserves accuracy within standard error of the BF16 source on every task, and matches the source on TruthfulQA. The Claude Opus reasoning gain over the Qwen 3.6 base (+20 pp on GSM8K) is retained.

Throughput

Measured on a single NVIDIA B300 SXM6 with vLLM 0.21.0 and torch.compile enabled:

Setup	Throughput	Speedup
Batch = 1, no MTP	115 tok/s	1.00×
Batch = 1, MTP `num_speculative_tokens = 3`	251 tok/s	2.19×
Batch = 8 continuous batching, no MTP	880 tok/s	—

MTP speculative decoding hits an Avg Draft acceptance rate of ~77 % (per-position: 0.92 / 0.79 / 0.65) with a mean acceptance length of ~3.3, measured on a mixed reasoning + code prompt set at greedy decoding.

Self-test of tool calling with --tool-call-parser qwen3_coder: passes (model emits well-formed <tool_call>...</tool_call> syntax).

Quantization

Precision	Modules
INT4 asymmetric, group_size = 128	`q_proj`, `k_proj`, `v_proj`, MLP `gate_proj`, MLP `up_proj`, DeltaNet `in_proj_qkv`, `in_proj_z`
BF16	`o_proj`, MLP `down_proj`, `lm_head`, `embed_tokens`, norms, DeltaNet small projections (`in_proj_a`, `in_proj_b`), DeltaNet `out_proj`, vision tower, multimodal projector, 1-layer MTP head, first 5 and last 5 transformer layers (APEX edge protection)

The AWQ skip list also names every mtp.* linear module explicitly so the MTP draft head stays unquantized — previous revisions of this checkpoint omitted those entries, which caused vLLM to build the MTP head with AWQ-packed parameters and produced 0 % draft acceptance.

Tuned with AutoRound on 1024 self-generated reasoning traces (200 iterations per block, batch_size=1).

Calibration data: 1024 self-generated traces from the BF16 source model (256 prompts × 4 generations) covering math, code, logic, analysis, creative writing, general knowledge, tool calling, and Korean.

Files

File	Size	Purpose
`model-*.safetensors` (13 shards)	~25 GB	Main quantized weights
`model_extra_tensors.safetensors`	~1 GB	MTP head + edge-protected layers (BF16)
`quantization_config.json`	<1 KB	AWQ config (`quant_method=awq`, `bits=4`, `group_size=128`, `zero_point=true`) with BF16 MTP skip entries
`config.json` + tokenizer + processor configs	<100 MB	Standard metadata

Total checkpoint size: ~26 GB (down from ~54 GB BF16 source).

License

Apache 2.0 (inherited from the base model).

Downloads last month: 8,148

Safetensors

Model size

11B params

Tensor type

I32

BF16

Model tree for mconcat/Qwopus3.6-27B-v2-AWQ-4bit

Base model

Jackrong/Qwopus3.6-27B-v2

Quantized

(38)

this model