Instructions to use PrimeIntellect/Qwen3.5-0.8B-Reverse-Text-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PrimeIntellect/Qwen3.5-0.8B-Reverse-Text-SFT with Transformers:

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

pipe = pipeline("image-text-to-text", model="PrimeIntellect/Qwen3.5-0.8B-Reverse-Text-SFT")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("PrimeIntellect/Qwen3.5-0.8B-Reverse-Text-SFT")
model = AutoModelForMultimodalLM.from_pretrained("PrimeIntellect/Qwen3.5-0.8B-Reverse-Text-SFT", device_map="auto")
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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use PrimeIntellect/Qwen3.5-0.8B-Reverse-Text-SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PrimeIntellect/Qwen3.5-0.8B-Reverse-Text-SFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PrimeIntellect/Qwen3.5-0.8B-Reverse-Text-SFT",
		"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/PrimeIntellect/Qwen3.5-0.8B-Reverse-Text-SFT

SGLang

How to use PrimeIntellect/Qwen3.5-0.8B-Reverse-Text-SFT 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 "PrimeIntellect/Qwen3.5-0.8B-Reverse-Text-SFT" \
    --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": "PrimeIntellect/Qwen3.5-0.8B-Reverse-Text-SFT",
		"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 "PrimeIntellect/Qwen3.5-0.8B-Reverse-Text-SFT" \
        --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": "PrimeIntellect/Qwen3.5-0.8B-Reverse-Text-SFT",
		"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 PrimeIntellect/Qwen3.5-0.8B-Reverse-Text-SFT with Docker Model Runner:
```
docker model run hf.co/PrimeIntellect/Qwen3.5-0.8B-Reverse-Text-SFT
```

Qwen3.5-0.8B-Reverse-Text-SFT / README.md

Fareso

Model card: recipe inline, status, caveat

09b3d0a verified 6 days ago

preview code

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	---
	license: apache-2.0
	base_model:
	- Qwen/Qwen3.5-0.8B
	datasets:
	- PrimeIntellect/Reverse-Text-SFT
	library_name: transformers
	tags:
	- prime-rl
	- ci
	---

	# Qwen3.5-0.8B-Reverse-Text-SFT

	A short SFT fine-tune of [`Qwen/Qwen3.5-0.8B`](https://huggingface.co/Qwen/Qwen3.5-0.8B) on [`PrimeIntellect/Reverse-Text-SFT`](https://huggingface.co/datasets/PrimeIntellect/Reverse-Text-SFT).
	It is a small, deliberately under-trained starting point for `reverse-text` RL, made for the planned move of the [prime-rl](https://github.com/PrimeIntellect-ai/prime-rl) CI RL tests from `PrimeIntellect/Qwen3-0.6B-Reverse-Text-SFT` to Qwen3.5. It is not meant for general use.

	## Recipe
	- Code: prime-rl commit [`21814b401`](https://github.com/PrimeIntellect-ai/prime-rl/commit/21814b401e29240e8ca4f161dafa0bba6d1425ba) (branch `ci/qwen3_5-ci`, contains the Qwen3.5 tied `lm_head` fix #3863 and the CP fix #3864).
	- Config (`uv run sft @ sft.toml`), 1 H200:
	```toml
	max_steps = 10

	[model]
	name = "Qwen/Qwen3.5-0.8B"

	[data]
	name = "PrimeIntellect/Reverse-Text-SFT"
	seq_len = 4096
	batch_size = 32

	[optim]
	lr = 2e-5
	```
	- Chat template: unchanged Qwen3.5 template, thinking off (the 0.8B default). Completions are rendered with the empty `<think>\n\n</think>\n\n` prefix, the same as RL generation.
	- Weights include the (frozen, unchanged) vision tower, so the checkpoint loads as `Qwen3_5ForConditionalGeneration` like the base model.

	## Numbers
	- SFT loss, steps 1-10: 4.98, 6.16, 5.59, 4.96, 4.53, 4.22, 3.96, 3.62, 3.27, 2.92.
	- `reverse-text` eval reward (LCS ratio, 256 prompts, temperature 1, 128 max tokens): 0.31 (base model: 0.03 on 32 prompts).