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)

# pip install -U transformers accelerate
# 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=256)
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_5MoeForConditionalGeneration
- Input: Text
- Output: Text
Source Model: Qwen3.6-35B-A3B
Supported Hardware: AMD EPYC (CPU inference)
Preferred Operating System: Linux
Inference Engine: vLLM v0.28.0
Quantization Framework: LLM Compressor v0.12.0
Quantization Method: 4-bit Weight-Only Quantization (W4A16)
Compatible Stack:
- ZenDNN v6.1.0
- ZenTorch v2.13.0.0
- PyTorch v2.13.0.0
- LLM Compressor v0.12.0
- vLLM v0.28.0

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, valid here because every quantized weight's column count (2048 hidden, 512 MoE intermediate) divides by 128
Calibration: 128 examples from HuggingFaceH4/ultrachat_200k at a max sequence length of 2048
Kept in BF16: the vision tower (model.visual), the MoE router (mlp.gate), shared_expert_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.

Three details in the recipe are specific to this model. The model is loaded with AutoModelForImageTextToText rather than AutoModelForCausalLM, so vision_config and the Qwen3_5MoeForConditionalGeneration metadata survive into the saved config for vLLM 0.26+; without it the config is demoted to the text-only variant and vLLM rejects the checkpoint. shared_expert_gate is skipped because it has out_features=1 and vLLM expects a dense .weight there rather than a packed one. And the router is skipped because it is a tiny Linear whose logits decide expert assignment, where a mis-routed token costs far more accuracy than 4-bit expert weights do.

import json
import os

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

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

MODEL_ID = "Qwen/Qwen3.6-35B-A3B"
OUTPUT_DIR = "./Qwen3.6-35B-A3B-w4a16-llmcompressor-v0.12.0"
CALIB_SIZE = 128
MAX_SEQ_LENGTH = 2048

IGNORE = [
    "lm_head",
    r"re:.*\.router$",
    r"re:.*\.router\..*",
    r"re:.*\.gate$",
    r"re:.*\.mlp\.gate$",
    "shared_expert_gate",
    r"re:.*\.shared_expert_gate(\.|$)",
    r"re:.*\.visual(\.|$)",
    r"re:.*\.visual\..*",
]

# Step 1: Load the BF16 model with the multimodal wrapper so vision_config and
# the ForConditionalGeneration architecture are preserved on save.
model = AutoModelForImageTextToText.from_pretrained(
    MODEL_ID,
    dtype=torch.bfloat16,
    device_map="cpu",
    trust_remote_code=True,
)
reference_config = model.config
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 ex: {"text": "\n".join(m["content"] for m in ex["messages"] if m.get("content"))},
    remove_columns=ds.column_names,
)
if not getattr(tokenizer, "pad_token", None):
    tokenizer.pad_token = tokenizer.eos_token
calib_ds = ds.map(
    lambda ex: tokenizer(
        ex["text"], truncation=True, max_length=MAX_SEQ_LENGTH, add_special_tokens=False
    ),
    remove_columns=["text"],
)

# Step 3: Define the W4A16 GPTQ recipe and one-shot quantize. Passing
# processor=MODEL_ID saves the processor configs alongside the model.
recipe = GPTQModifier(targets="Linear", scheme="W4A16", ignore=IGNORE)
oneshot(
    model=model,
    dataset=calib_ds,
    recipe=recipe,
    max_seq_length=MAX_SEQ_LENGTH,
    processor=MODEL_ID,
    output_dir=OUTPUT_DIR,
    save_compressed=True,
    trust_remote_code_model=True,
)

# Step 4: Patch config.json so architectures/vision_config stay intact for vLLM.
config_path = os.path.join(OUTPUT_DIR, "config.json")
with open(config_path, encoding="utf-8") as f:
    saved = json.load(f)
full = reference_config.to_dict()
for key in (
    "architectures",
    "model_type",
    "text_config",
    "vision_config",
    "vision_start_token_id",
    "vision_end_token_id",
    "image_token_id",
    "video_token_id",
):
    if full.get(key) is not None:
        saved[key] = full[key]
with open(config_path, "w", encoding="utf-8") as f:
    json.dump(saved, f, indent=2)
    f.write("\n")

# 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 standalone script:

numactl --physcpubind=0-95 python quant-qwen3.6-w4a16.py

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==2.13.0.0
zentorch==2.13.0.0
vllm==0.28.0
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	0.9613	99.61%

Evaluation Command

lm_eval \
    --model vllm \
    --model_args pretrained=amd/Qwen3.6-35B-A3B-w4a16-llmcompressor-v0.12.0,tokenizer=Qwen/Qwen3.6-35B-A3B,dtype=bfloat16,max_model_len=4096,language_model_only=True,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 v2.13.0.0 / PyTorch v2.13.0.0. 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.
Vision Path Unquantized: The vision tower stays in BF16, so image preprocessing cost is unchanged and the memory saving comes entirely from the language model. Evaluation was run with language_model_only=True.
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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Tensor type

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BF16

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