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
```

ov_intent_analysis_sft / README.md

guoxuter

Publish v7 Safetensors checkpoint

687ba14 verified 6 days ago

preview code

Raw

History Blame Contribute Delete

2.31 kB

	---
	library_name: transformers
	license: apache-2.0
	base_model:
	- Qwen/Qwen3.5-0.8B
	pipeline_tag: text-generation
	language:
	- zh
	- en
	tags:
	- qwen3_5
	- sft
	- openviking
	- intent-analysis
	- query-planning
	---

	# 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`](https://ollama.com/guoxuter/ov_intent_analysis_sft:v7_q8).

	## Loading

	Use a recent Transformers version with Qwen3.5 support:

	```python
	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`](https://huggingface.co/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.