Instructions to use compute1/Qwen3.8-27B-W8A8-INT8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use compute1/Qwen3.8-27B-W8A8-INT8 with Transformers:

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

pipe = pipeline("text-generation", model="compute1/Qwen3.8-27B-W8A8-INT8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("compute1/Qwen3.8-27B-W8A8-INT8")
model = AutoModelForCausalLM.from_pretrained("compute1/Qwen3.8-27B-W8A8-INT8", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use compute1/Qwen3.8-27B-W8A8-INT8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "compute1/Qwen3.8-27B-W8A8-INT8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "compute1/Qwen3.8-27B-W8A8-INT8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/compute1/Qwen3.8-27B-W8A8-INT8

SGLang

How to use compute1/Qwen3.8-27B-W8A8-INT8 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 "compute1/Qwen3.8-27B-W8A8-INT8" \
    --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": "compute1/Qwen3.8-27B-W8A8-INT8",
		"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 "compute1/Qwen3.8-27B-W8A8-INT8" \
        --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": "compute1/Qwen3.8-27B-W8A8-INT8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use compute1/Qwen3.8-27B-W8A8-INT8 with Docker Model Runner:
```
docker model run hf.co/compute1/Qwen3.8-27B-W8A8-INT8
```

Qwen3.8-27B W8A8 INT8 (compressed-tensors)

Quantized from Qwen/Qwen3.8-27B using llm-compressor.

Quantization Config

Format: int-quantized (compressed-tensors)
Weights: INT8, channel-wise, symmetric, MSE observer
Activations: INT8, token-wise, symmetric, dynamic
Ignored: lm_head, embeddings, vision encoder, MTP layers, GDN linear-attn sub-modules (in_proj_a, in_proj_b, conv1d, norm, A_log, dt_bias), all layernorms, q_norm, k_norm

Architecture

Qwen3.8-27B uses a hybrid architecture with 48 Gated DeltaNet (GDN) linear-attention layers and 16 full-attention layers (every 4th layer). The GDN in_proj_a/in_proj_b projections are kept in BF16 because they require special handling for fused loading.

Intended Use

Optimized for inference on Ampere GPUs (RTX 3090, A100) with SGLang/vLLM Marlin kernels. Weight-only INT8 with BF16 activations.

Downloads last month: 518

Safetensors

Model size

27B params

Tensor type

BF16

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