Instructions to use milwright/cloze-reader-qwen3.5-0.8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use milwright/cloze-reader-qwen3.5-0.8b with Transformers:

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

pipe = pipeline("text-generation", model="milwright/cloze-reader-qwen3.5-0.8b")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("milwright/cloze-reader-qwen3.5-0.8b")
model = AutoModelForMultimodalLM.from_pretrained("milwright/cloze-reader-qwen3.5-0.8b")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use milwright/cloze-reader-qwen3.5-0.8b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "milwright/cloze-reader-qwen3.5-0.8b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "milwright/cloze-reader-qwen3.5-0.8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/milwright/cloze-reader-qwen3.5-0.8b

SGLang

How to use milwright/cloze-reader-qwen3.5-0.8b 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 "milwright/cloze-reader-qwen3.5-0.8b" \
    --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": "milwright/cloze-reader-qwen3.5-0.8b",
		"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 "milwright/cloze-reader-qwen3.5-0.8b" \
        --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": "milwright/cloze-reader-qwen3.5-0.8b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use milwright/cloze-reader-qwen3.5-0.8b 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 milwright/cloze-reader-qwen3.5-0.8b 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 milwright/cloze-reader-qwen3.5-0.8b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for milwright/cloze-reader-qwen3.5-0.8b to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="milwright/cloze-reader-qwen3.5-0.8b",
    max_seq_length=2048,
)

Docker Model Runner
How to use milwright/cloze-reader-qwen3.5-0.8b with Docker Model Runner:
```
docker model run hf.co/milwright/cloze-reader-qwen3.5-0.8b
```

cloze-reader-qwen3.5-0.8b (merged)

Merged bf16 build of Qwen/Qwen3.5-0.8B + the rank-16 cloze-reader LoRA, for serving without applying the adapter at load. It is the language-model backend for the Cloze Reader reading-comprehension game — four tightly-constrained tasks: word selection, batch word selection, contextual hints, and one-sentence literary contextualization.

For training details, evaluation, task prompts, and limitations, see the adapter model card — this repo is the same fine-tune, pre-merged. ~1.7 GB at bf16.

Serve with vLLM

vllm serve milwright/cloze-reader-qwen3.5-0.8b \
    --served-model-name cloze-reader \
    --host 127.0.0.1 --port 1234 \
    --dtype bfloat16 --max-model-len 2048

Then POST OpenAI-shape chat completions with "model": "cloze-reader". To co-serve several inference-arcade apps from one base, use the adapter repo with vLLM's --enable-lora instead (see the adapter card).

Load with transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

REPO = "milwright/cloze-reader-qwen3.5-0.8b"
tok = AutoTokenizer.from_pretrained(REPO)
model = AutoModelForCausalLM.from_pretrained(REPO, torch_dtype=torch.bfloat16, device_map="auto")

License

Apache 2.0, inheriting from Qwen3.5-0.8B. Training data includes public-domain Project Gutenberg passages and distilled outputs from google/gemma-3-27b-it and google/gemini-3.1-flash-lite-preview.

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Safetensors

Model size

0.9B params

Tensor type

F32

BF16

Model tree for milwright/cloze-reader-qwen3.5-0.8b

Base model

Qwen/Qwen3.5-0.8B-Base

Finetuned

Qwen/Qwen3.5-0.8B

Finetuned

(270)

this model

milwright
/

cloze-reader-qwen3.5-0.8b

cloze-reader-qwen3.5-0.8b (merged)

Serve with vLLM

Load with transformers

License

Model tree for milwright/cloze-reader-qwen3.5-0.8b

Dataset used to train milwright/cloze-reader-qwen3.5-0.8b