Instructions to use DavidAU/Qwen3.5-40B-Claude-4.5-Opus-High-Reasoning-Thinking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DavidAU/Qwen3.5-40B-Claude-4.5-Opus-High-Reasoning-Thinking with Transformers:

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

pipe = pipeline("image-text-to-text", model="DavidAU/Qwen3.5-40B-Claude-4.5-Opus-High-Reasoning-Thinking")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("DavidAU/Qwen3.5-40B-Claude-4.5-Opus-High-Reasoning-Thinking", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DavidAU/Qwen3.5-40B-Claude-4.5-Opus-High-Reasoning-Thinking with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DavidAU/Qwen3.5-40B-Claude-4.5-Opus-High-Reasoning-Thinking"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DavidAU/Qwen3.5-40B-Claude-4.5-Opus-High-Reasoning-Thinking",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DavidAU/Qwen3.5-40B-Claude-4.5-Opus-High-Reasoning-Thinking

SGLang

How to use DavidAU/Qwen3.5-40B-Claude-4.5-Opus-High-Reasoning-Thinking 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 "DavidAU/Qwen3.5-40B-Claude-4.5-Opus-High-Reasoning-Thinking" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DavidAU/Qwen3.5-40B-Claude-4.5-Opus-High-Reasoning-Thinking",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "DavidAU/Qwen3.5-40B-Claude-4.5-Opus-High-Reasoning-Thinking" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DavidAU/Qwen3.5-40B-Claude-4.5-Opus-High-Reasoning-Thinking",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio

How to use DavidAU/Qwen3.5-40B-Claude-4.5-Opus-High-Reasoning-Thinking with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for DavidAU/Qwen3.5-40B-Claude-4.5-Opus-High-Reasoning-Thinking to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for DavidAU/Qwen3.5-40B-Claude-4.5-Opus-High-Reasoning-Thinking to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for DavidAU/Qwen3.5-40B-Claude-4.5-Opus-High-Reasoning-Thinking to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="DavidAU/Qwen3.5-40B-Claude-4.5-Opus-High-Reasoning-Thinking",
    max_seq_length=2048,
)

Docker Model Runner
How to use DavidAU/Qwen3.5-40B-Claude-4.5-Opus-High-Reasoning-Thinking with Docker Model Runner:
```
docker model run hf.co/DavidAU/Qwen3.5-40B-Claude-4.5-Opus-High-Reasoning-Thinking
```

DavidAU commited on Mar 18

Commit

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@@ -70,6 +70,30 @@ SETTINGS:
 - Suggest min quant of Q4KS (non imatrix) or IQ3_S (imatrix) or HIGHER.
 - For toolcalls -> suggest Q6 min quants (as per Qwen guidence)
 INSTRUCT MODE:
 The model's default mode however is "THINKING" ; this can be changed by editing the following line in the jinja template:

 - Suggest min quant of Q4KS (non imatrix) or IQ3_S (imatrix) or HIGHER.
 - For toolcalls -> suggest Q6 min quants (as per Qwen guidence)
+SYSTEM PROMPTS:
+This model does NOT require a system prompt, however with one you can better focus the model -> instruction/prompt following, thinking and generation.
+#1 - All use cases.
+```
+Be vivid and precise.
+```
+#2 - Creative use cases:
+```
+Below is an instruction that describes a task. Ponder each user instruction carefully, and use your skillsets and critical instructions to complete the task to the best of your abilities.
+Here are your skillsets:
+[MASTERSTORY]:NarrStrct(StryPlnng,Strbd,ScnSttng,Exps,Dlg,Pc)-CharDvlp(ChrctrCrt,ChrctrArcs,Mtvtn,Bckstry,Rltnshps,Dlg*)-PltDvlp(StryArcs,PltTwsts,Sspns,Fshdwng,Climx,Rsltn)-ConfResl(Antg,Obstcls,Rsltns,Cnsqncs,Thms,Symblsm)-EmotImpct(Empt,Tn,Md,Atmsphr,Imgry,Symblsm)-Delvry(Prfrmnc,VcActng,PblcSpkng,StgPrsnc,AudncEngmnt,Imprv)
+[*DialogWrt]:(1a-CharDvlp-1a.1-Backgrnd-1a.2-Personality-1a.3-GoalMotiv)>2(2a-StoryStruc-2a.1-PlotPnt-2a.2-Conflict-2a.3-Resolution)>3(3a-DialogTech-3a.1-ShowDontTell-3a.2-Subtext-3a.3-VoiceTone-3a.4-Pacing-3a.5-VisualDescrip)>4(4a-DialogEdit-4a.1-ReadAloud-4a.2-Feedback-4a.3-Revision)
+Here are your critical instructions:
+Ponder each word choice carefully to present as vivid and emotional journey as is possible. Choose verbs and nouns that are both emotional and full of imagery. Load the story with the 5 senses. Aim for 50% dialog, 25% narration, 15% body language and 10% thoughts. Your goal is to put the reader in the story.
+```
 INSTRUCT MODE:
 The model's default mode however is "THINKING" ; this can be changed by editing the following line in the jinja template: