Instructions to use Chungulus/Qwen3.8-27B-GPTQ-INT4-G32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Local Apps Settings

How to use Chungulus/Qwen3.8-27B-GPTQ-INT4-G32 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Chungulus/Qwen3.8-27B-GPTQ-INT4-G32"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Chungulus/Qwen3.8-27B-GPTQ-INT4-G32",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Chungulus/Qwen3.8-27B-GPTQ-INT4-G32

SGLang

How to use Chungulus/Qwen3.8-27B-GPTQ-INT4-G32 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 "Chungulus/Qwen3.8-27B-GPTQ-INT4-G32" \
    --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": "Chungulus/Qwen3.8-27B-GPTQ-INT4-G32",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "Chungulus/Qwen3.8-27B-GPTQ-INT4-G32" \
        --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": "Chungulus/Qwen3.8-27B-GPTQ-INT4-G32",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use Chungulus/Qwen3.8-27B-GPTQ-INT4-G32 with Docker Model Runner:
```
docker model run hf.co/Chungulus/Qwen3.8-27B-GPTQ-INT4-G32
```

Qwen3.8-27B GPTQ-INT4-G32

This repository is a quantized deployment variant of the official Qwen/Qwen3.8-27B model. It is not a fine-tune.

Provenance and configuration

Exact parent revision: 1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0
Quantizer: GPTQModel
Quantizer revision: 7df3d1e837cb5552512bf99b8729c334d0b2c010
Quantization configuration: {"bits": 4, "calibration_batch_size": 1, "calibration_corpus_sha256": "7ac4e3f0092bd5acfdeaec88f19a8c829458b0716c10ab9e81cda6f8b064b675", "calibration_samples": 128, "calibration_sequence_length": 2048, "calibration_sort": true, "checkpoint_format": "gptq", "desc_act": true, "format": "gptq", "group_size": 32, "lm_head": false, "meta": {"act_group_aware": false, "auto_forward_data_parallel": true, "dense_vram_strategy": "exclusive", "dense_vram_strategy_devices": null, "fallback": {"smooth": null, "strategy": "rtn", "threshold": "0.5%"}, "gc_mode": "interval", "gptaq": null, "hessian": {"chunk_bytes": null, "chunk_size": null, "staging_dtype": "float32"}, "mock_quantization": false, "moe_vram_strategy": "exclusive", "moe_vram_strategy_devices": null, "mse": 0.0, "offload_to_disk": false, "offload_to_disk_path": null, "pack_impl": "cpu", "wait_for_submodule_finalizers": false}, "method": "gptq", "pack_dtype": "int32", "quant_method": "gptq", "sym": true}
Artifact size: 19.565 GiB
Supported runtime: GPTQModel 7.3.4+
Tested hardware: NVIDIA GeForce RTX 4070 12 GB (Ada, SM89), WSL2 Ubuntu 24.04

Calibration and architecture handling

The calibration corpus is original CC0-1.0 material authored for this project. It contains 216 text/tool samples and 8 vision samples across prose, code, debugging, math, science, structured output, tools, multilingual, and long-context categories. This build used calibration/core.jsonl:128.

Tensor exclusions: ["*.input_layernorm", "*.post_attention_layernorm", "*.self_attn.q_norm", "*.self_attn.k_norm", "*.linear_attn.norm", "*.linear_attn.conv1d", "*.linear_attn.in_proj_a", "*.linear_attn.in_proj_b"]
Vision: True (deterministic image generation gate)
Tool use: True (schema-valid XML tool-call gate)
MTP: True for preservation of the 15 external tensors; no local speculative drafter/acceptance runtime is claimed.
Processor/chat template: exact pinned-parent copy verified.

Numerical validation

Full-vocabulary held-out comparison used 512 token positions.

Metric	Result
Mean KL	0.01732363
Median KL	0.00748368
P95 KL	0.05911852
Perplexity delta	+3.2276%
Top-1 agreement	0.933594
Top-5 set overlap	0.924609
Logit cosine similarity	0.99574828

Performance smoke

Execution mode: RTX 4070 + CPU offload/auto map
Device map summary: {"cpu": 53, "cuda:0": 15}
Prompt/decode: 512 / 8 tokens
Median TTFT: 53.3875 s
Median prefill: 9.556 tok/s
Median decode: 0.057 tok/s
Cold/warm load: 155.154 / 150.329 s
Peak physical device memory: 11.672 GiB
PyTorch logical allocator peak (diagnostic, may exceed physical VRAM under WSL): 15.766 GiB
Peak build RAM / VRAM: 19.263 / 10.301 GiB

Limitations

The RTX 4070 validation may use CPU offload and is not a pure-GPU throughput claim unless the performance mode explicitly says so. Preserved MTP weights do not establish speculative decoding support. Compatibility and quality are established only for the exact revision and runtime above. Machine-readable structural, generation, vision, tools, numerical, and performance reports are stored under validation/ in this repository. Remote verification evidence is retained by the build worker and tied to the exact repository revision.