Instructions to use newzyerror/fluidity-vapour-4.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use newzyerror/fluidity-vapour-4.1 with Transformers:

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

pipe = pipeline("text-generation", model="newzyerror/fluidity-vapour-4.1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("newzyerror/fluidity-vapour-4.1")
model = AutoModelForCausalLM.from_pretrained("newzyerror/fluidity-vapour-4.1")
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 newzyerror/fluidity-vapour-4.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "newzyerror/fluidity-vapour-4.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "newzyerror/fluidity-vapour-4.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/newzyerror/fluidity-vapour-4.1

SGLang

How to use newzyerror/fluidity-vapour-4.1 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 "newzyerror/fluidity-vapour-4.1" \
    --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": "newzyerror/fluidity-vapour-4.1",
		"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 "newzyerror/fluidity-vapour-4.1" \
        --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": "newzyerror/fluidity-vapour-4.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use newzyerror/fluidity-vapour-4.1 with Docker Model Runner:
```
docker model run hf.co/newzyerror/fluidity-vapour-4.1
```

fluidity-vapour-4.2

Introduction

We're introducing fluidity-vapour-4.1, our latest flagship model for long-horizon tasks. It marks a substantial leap in long-horizon task capability over its predecessor fluidity-vapour-4.1 and, for the first time, delivers that capability on a solid 1M-token context. fluidity-vapour-4.2 new capabilities include:

Solid 1M Context: A solid 1M-token context that stably sustains long-horizon work
Advanced Coding with Flexible Effort: Stronger coding capabilities with multiple thinking effort levels to balance performance and latency
Improved Architecture: We propose IndexShare, which reuses the same indexer across every four sparse attention layers, reducing per-token FLOPs by 2.9× at a 1M context length. We also improve GLM-5.2’s MTP layer for speculative decoding, increasing the acceptance length by up to 20%
Pure Open: An MIT open-source license — no regional limits, technical access without borders

https://raw.githubusercontent.com/FluidityVapour/FV-4.2/refs/heads/main/resources/water_drop.png

Benchmark

BenchmarkFluidity-Vapour 4.2GLM-5.1Qwen3.7-MaxMiniMax M3DeepSeek-V4-ProClaude Opus 4.8GPT-5.5Gemini 3.1 ProReasoningHLE40.53141.43737.749.841.445HLE (w/ Tools)54.752.353.5-48.257.952.251.4*CritPt20.94.613.43.712.920.927.117.7AIME 202699.295.397-94.695.798.398.2HMMT Nov. 202594.4949584.494.496.596.594.8HMMT Feb. 202692.582.697.184.495.296.796.787.3IMOAnswerBench91.083.890-89.883.5-81GPQA-Diamond91.286.2909390.193.693.694.3CodingSWE-bench Pro62.158.460.65955.469.258.654.2NL2Repo48.942.747.242.135.569.750.733.4DeepSWE46.21818208587010ProgramBench63.750.9--47.871.970.839.5Terminal Bench 2.1 (Terminus-2)81.063.5756564858474Terminal Bench 2.1 (Best Reported Harness)82.769---78.983.470.7FrontierSWE (Dominance)74.430.5--29.075.172.639.6PostTrainBench34.320.1---37.228.421.6SWE-marathon13.01.0---26.012.04.0AgenticMCP-Atlas (Public Set)76.871.876.474.273.677.875.369.2Tool-Decathlon48.240.7--52.859.955.648.8

Serve fluidity-vapour-4.2 Locally

fluidity-vapour-4.2 supports deployment with the following frameworks. Feel free to try them out:

SGLang (v0.5.13.post1+) — see cookbook
vLLM (v0.23.0+) — see recipes
Transformers (v0.5.12+) — see transformers docs
KTransformers (v0.5.12+) — see tutorial
Unsloth (v0.1.47-beta+) — see guide
For deployment on the Ascend NPU platform, inference frameworks such as vLLM-Ascend, xLLM and SGLang are supported — see here.

Citation

If you find fluidity-vapour-4.2 useful in your research, please cite our technical report:

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Safetensors

Model size

753B params

Tensor type

BF16

F32

Dataset used to train newzyerror/fluidity-vapour-4.1

Paper for newzyerror/fluidity-vapour-4.1

IndexCache: Accelerating Sparse Attention via Cross-Layer Index Reuse

Paper • 2603.12201 • Published Mar 12 • 60