Instructions to use guoxuter/ov_intent_analysis_sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use guoxuter/ov_intent_analysis_sft with Transformers:

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

pipe = pipeline("text-generation", model="guoxuter/ov_intent_analysis_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)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("guoxuter/ov_intent_analysis_sft")
model = AutoModelForMultimodalLM.from_pretrained("guoxuter/ov_intent_analysis_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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use guoxuter/ov_intent_analysis_sft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "guoxuter/ov_intent_analysis_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": "guoxuter/ov_intent_analysis_sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/guoxuter/ov_intent_analysis_sft

SGLang

How to use guoxuter/ov_intent_analysis_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 "guoxuter/ov_intent_analysis_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": "guoxuter/ov_intent_analysis_sft",
		"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 "guoxuter/ov_intent_analysis_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": "guoxuter/ov_intent_analysis_sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use guoxuter/ov_intent_analysis_sft with Docker Model Runner:
```
docker model run hf.co/guoxuter/ov_intent_analysis_sft
```

OpenViking Intent Analysis SFT v7

ov_intent_analysis_sft is a Qwen3.5-0.8B model fine-tuned for OpenViking retrieval intent analysis and query planning. Given recent conversation context and the current user message, it decides whether retrieval is needed and emits structured queries targeting OpenViking skill, resource, and memory scopes.

This repository contains the original Transformers checkpoint in Safetensors format. The corresponding quantized Ollama release is guoxuter/ov_intent_analysis_sft:v7_q8.

Loading

Use a recent Transformers version with Qwen3.5 support:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "guoxuter/ov_intent_analysis_sft"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    dtype="auto",
    device_map="auto",
    trust_remote_code=True,
)

The model was trained for the OpenViking v7 retrieval prompt and structured output contract. For end-to-end use, prefer the prompt bundled with OpenViking rather than a generic chat prompt.

Artifact provenance

Base model: Qwen/Qwen3.5-0.8B
Training checkpoint: step 600 of the v7 SFT run
Safetensors SHA-256: c92c878a96f34d2f0c87d2308099de7dc1401aae58aba0310aca550e9024b33b
Corresponding Ollama Q8 GGUF layer SHA-256: aa98adccdec6a3be82d462563586abe1db520f93281ecc3f9bc3ff978b12d795

The Safetensors checkpoint is the source artifact. The Ollama model is a Q8 GGUF derivative and should not be used to reconstruct full-precision weights.

Intended use

This model is intended as a compact retrieval planner for OpenViking-compatible systems. It is not a general-purpose assistant. Outputs should be validated against the expected structured schema before they are executed or used for retrieval.

License

The base Qwen3.5-0.8B model is released under the Apache License 2.0. This fine-tuned checkpoint is published under the same license.

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Safetensors

Model size

0.9B params

Tensor type

F32

BF16

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