Instructions to use amd/Qwen3.6-35B-A3B-w4a16-llmcompressor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use amd/Qwen3.6-35B-A3B-w4a16-llmcompressor with Transformers:

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

pipe = pipeline("text-generation", model="amd/Qwen3.6-35B-A3B-w4a16-llmcompressor")
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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("amd/Qwen3.6-35B-A3B-w4a16-llmcompressor")
model = AutoModelForMultimodalLM.from_pretrained("amd/Qwen3.6-35B-A3B-w4a16-llmcompressor", device_map="auto")
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 Settings

vLLM

How to use amd/Qwen3.6-35B-A3B-w4a16-llmcompressor with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "amd/Qwen3.6-35B-A3B-w4a16-llmcompressor"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amd/Qwen3.6-35B-A3B-w4a16-llmcompressor",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/amd/Qwen3.6-35B-A3B-w4a16-llmcompressor

SGLang

How to use amd/Qwen3.6-35B-A3B-w4a16-llmcompressor 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 "amd/Qwen3.6-35B-A3B-w4a16-llmcompressor" \
    --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": "amd/Qwen3.6-35B-A3B-w4a16-llmcompressor",
		"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 "amd/Qwen3.6-35B-A3B-w4a16-llmcompressor" \
        --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": "amd/Qwen3.6-35B-A3B-w4a16-llmcompressor",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use amd/Qwen3.6-35B-A3B-w4a16-llmcompressor with Docker Model Runner:
```
docker model run hf.co/amd/Qwen3.6-35B-A3B-w4a16-llmcompressor
```

Qwen3.6-35B-A3B-w4a16-llmcompressor-v0.12.0

Model Overview

Model Architecture: Qwen3_5MoeForCausalLM
- Input: Text
- Output: Text
Source Model: Qwen3.6-35B-A3B
Supported Hardware: AMD EPYC (CPU inference)
Preferred Operating System: Linux
Inference Engine: vLLM v0.26.0
Quantization Framework: LLM Compressor v0.12.0
Quantization Method: 4-bit Weight-Only Quantization (W4A16)
Compatible Stack:
- ZenDNN v6.1.0
- ZenTorch v2.11.0.3
- PyTorch v2.11.0
- LLM Compressor v0.12.0
- vLLM v0.26.0

This is a quantized version of Qwen3.6-35B-A3B created by AMD using LLM Compressor (compressed-tensors) for ZenDNN-optimized CPU inference.

Quantization

The model was quantized from Qwen3.6-35B-A3B using LLM Compressor via the GPTQ algorithm. This reduces the model weights from 67.0 GiB to 18.1 GiB on disk (~73% reduction).

Method: 4-bit Weight-Only Quantization (W4A16)
Config: compressed-tensors, num_bits=4, type=int, symmetric=true, group_size=128
Weights: INT4, symmetric, group-wise (group_size=128, actorder=static), stored as pack-quantized
Activations: BF16 (unquantized)
Group Size: 128, auto-selected as the largest of 128/64/32 that divides every quantizable weight's column count
Calibration: 128 examples from HuggingFaceH4/ultrachat_200k at a max sequence length of 2048
Kept in BF16: the MoE router (mlp.gate), lm_head, embed_tokens, and the layer norms. The 256 routed experts, the shared expert, and the linear-attention projections are all quantized, which is why the reduction is close to the 75% ideal for INT4.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from datasets import load_dataset

from llmcompressor import oneshot
from llmcompressor.modifiers.quantization.gptq import GPTQModifier

model_id = "Qwen/Qwen3.6-35B-A3B"
save_dir = "./Qwen3.6-35B-A3B-w4a16-llmcompressor-v0.12.0"

CALIB_SIZE = 128
MAX_SEQ_LENGTH = 2048

# Step 1: Load the BF16 model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="cpu",
    dtype=torch.bfloat16,
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)

# Step 2: Load calibration data. GPTQ is data-driven: it needs real activations to
# build the per-layer Hessians used to compensate the rounding error.
ds = load_dataset("HuggingFaceH4/ultrachat_200k", split=f"train_sft[:{CALIB_SIZE}]")
ds = ds.map(
    lambda example: {"text": "\n".join(m["content"] for m in example["messages"] if m["content"])},
    remove_columns=ds.column_names,
)

# Step 3: Define the W4A16 GPTQ recipe. Routing layers are skipped: they are tiny,
# and a mis-routed token costs far more accuracy than 4-bit expert weights do.
# The W4A16 preset implies group_size=128, which is valid here because every
# quantized weight's column count (2048 hidden, 512 MoE intermediate) divides by 128.
recipe = GPTQModifier(
    targets="Linear",
    scheme="W4A16",
    ignore=[
        "lm_head",
        r"re:.*\.router$",
        r"re:.*\.router\..*",
        r"re:.*\.gate$",
        r"re:.*\.mlp\.gate$",
    ],
)

# Step 4: One-shot quantize with calibration and save in compressed-tensors format
oneshot(
    model=model,
    dataset=ds,
    recipe=recipe,
    max_seq_length=MAX_SEQ_LENGTH,
    processor=tokenizer,
)
model.save_pretrained(save_dir, save_compressed=True)
tokenizer.save_pretrained(save_dir)

# Smoke test
inputs = tokenizer("What are we having for dinner?", return_tensors="pt")
output = model.generate(**inputs, max_new_tokens=30)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Run end to end with the driver script:

numactl --physcpubind=0-95 python llm_compressor_quantize_and_run.py \
    --model_id Qwen/Qwen3.6-35B-A3B \
    --save_dir ./Qwen3.6-35B-A3B-w4a16-llmcompressor-v0.12.0 \
    --recipe gptq \
    --scheme W4A16 \
    --run

Quick Start

Use with vLLM

from vllm import LLM, SamplingParams

model = LLM(
    model="amd/Qwen3.6-35B-A3B-w4a16-llmcompressor-v0.12.0",
    dtype="bfloat16",
)

sampling_params = SamplingParams(temperature=0.7, max_tokens=256)
outputs = model.generate(["Hello, how are you?"], sampling_params)
print(outputs[0].outputs[0].text)

Requirements

torch==2.11.0
zentorch==2.11.0.3
vllm==0.26.0
llmcompressor==0.12.0

OpenMP Setup

For optimal performance, set LD_PRELOAD with libomp.so (LLVM OpenMP) or libiomp5.so (Intel OpenMP):

# Using LLVM OpenMP (llvmopenmp)
export LD_PRELOAD=$(find /path/to/env -name "libomp.so" | head -1)

# Or using Intel OpenMP (libiomp)
export LD_PRELOAD=$(find /path/to/env -name "libiomp5.so" | head -1)

Note: Set LD_PRELOAD before launching vLLM or any inference script.

Evaluation

The model was evaluated against the BF16 (unquantized) baseline on standard benchmarks using lm-evaluation-harness with the vLLM engine.

Benchmark	BF16 Baseline	W4A16 (this model)	Recovery
GSM8K (5-shot)	0.9651	0.9636	99.84%

Evaluation results will be updated after benchmarking.

Evaluation Command

lm_eval \
    --model vllm \
    --model_args pretrained=amd/Qwen3.6-35B-A3B-w4a16-llmcompressor,tokenizer=Qwen/Qwen3.6-35B-A3B,dtype=bfloat16,max_model_len=4096,enable_thinking=False \
    --tasks gsm8k \
    --batch_size auto \
    --trust_remote_code \
    --num_fewshot 5 \
    --apply_chat_template \
    --log_samples \
    --gen_kwargs "max_gen_toks=2048" \
    --output_path .

Limitations

Version Lock: This model is compatible with ZenDNN v6.1.0 / ZenTorch v2.11.0.3 / PyTorch v2.11.0. It may not load correctly on other versions.
CPU Only: This model is optimized for AMD EPYC CPU inference via ZenDNN. It is not intended for GPU inference.
Text-Only Checkpoint: The model was loaded through AutoModelForCausalLM during quantization, so the saved config is the text-only Qwen3_5MoeForCausalLM variant. The vision path of the source model is not carried over.
Reasoning Model: Qwen3.6 emits thinking traces by default. Evaluate with enable_thinking=False (and a generous max_gen_toks) so answer extraction stays comparable to the BF16 baseline.
Accuracy Trade-off: 4-bit weight-only quantization is more aggressive than INT8 and trades some accuracy for a ~73% smaller memory footprint.

License

This model is distributed under the same license as the source model. See the LICENSE file for details.

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Safetensors

Model size

35B params

Tensor type

I32

BF16

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