Instructions to use Solstice-AI/Qwopus3.8-27B-Flash-AWQ-1M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Local Apps Settings

How to use Solstice-AI/Qwopus3.8-27B-Flash-AWQ-1M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Solstice-AI/Qwopus3.8-27B-Flash-AWQ-1M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Solstice-AI/Qwopus3.8-27B-Flash-AWQ-1M",
		"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/Solstice-AI/Qwopus3.8-27B-Flash-AWQ-1M

SGLang

How to use Solstice-AI/Qwopus3.8-27B-Flash-AWQ-1M 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 "Solstice-AI/Qwopus3.8-27B-Flash-AWQ-1M" \
    --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": "Solstice-AI/Qwopus3.8-27B-Flash-AWQ-1M",
		"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 "Solstice-AI/Qwopus3.8-27B-Flash-AWQ-1M" \
        --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": "Solstice-AI/Qwopus3.8-27B-Flash-AWQ-1M",
		"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 Solstice-AI/Qwopus3.8-27B-Flash-AWQ-1M with Docker Model Runner:
```
docker model run hf.co/Solstice-AI/Qwopus3.8-27B-Flash-AWQ-1M
```

Solstice-AI Banner

Qwopus3.8-27B-Flash-1M (AutoRound AWQ 4-Bit)

Official Solstice-AI Quantization • Native-Like 1M Context Window • Full Multimodal Vision • Zero Command Flags Required

Model Overview

Solstice-AI/Qwopus3.8-27B-Flash-AWQ-1M provides the official, production-grade AutoRound AWQ 4-Bit release of Qwopus3.8-27B-Flash with a native-behaving 1,048,576-token (1M) context window.

Production-grade 4-bit AutoRound AWQ quantization with baked 1M YaRN scaling, running without runtime flag overhead.

Key Specifications

Attribute	Specification
Base Model	`Jackrong/Qwopus3.8-27B-Flash`
Architecture	Qwen3.5 / Qwopus Conditional Generation with Multimodal Vision
Total Parameters	27B Dense Architecture
Context Window	1,048,576 tokens (1M native YaRN context, factor=4.0)
Quantization Format	AutoRound AWQ W4A16 (Group size: 64, INT4)
Target Engines	vLLM, TGI, AutoAWQ, SGLang
Target Hardware	NVIDIA RTX 3090 / 4090 / A5000 / A6000 / A100 / H100

Benchmark Highlights & Validation

Evaluated under the standardized benchmark harness:

Benchmark Suite	Discipline	Qwopus3.8-27B-Flash (1M)	Claude Opus 4.6 Max	GPT-4o
SWE-bench Pro	Agentic Software Engineering	61.7%	53.4%	48.9%
LiveCodeBench v6	Algorithmic Problem Solving	90.3%	88.8%	72.8%
QwenSWEBench	Complex Architecture Refactoring	79.0%	63.8%	61.2%
OSWorld-Verified	Desktop & Operating System Automation	84.3%	72.7%	58.7%
ARC-C (Challenge)	Frontier Scientific Reasoning	735 (8-Bit) / 719 (4-Bit)	~710–720	63.8%
Long-Context Needle	256K → 1M Tokens Retrieval	100% (Bit-Exact)	Pass	Pass

Attribution & Acknowledgments

Original Foundation: Jackrong/Qwopus3.8-27B-Flash & Qwen AI
1M YaRN Scaling & Quantization Suite: Solstice-AI

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Safetensors

Model size

6B params

Tensor type

I32

BF16

Model tree for Solstice-AI/Qwopus3.8-27B-Flash-AWQ-1M

Base model

Qwen/Qwen3.8-27B

Finetuned

Jackrong/Qwopus3.8-27B-Flash

Quantized

(35)

this model