Instructions to use beita6969/SkillFlow-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use beita6969/SkillFlow-Model with Transformers:

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

pipe = pipeline("text-generation", model="beita6969/SkillFlow-Model")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("beita6969/SkillFlow-Model")
model = AutoModelForImageTextToText.from_pretrained("beita6969/SkillFlow-Model")
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

vLLM

How to use beita6969/SkillFlow-Model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "beita6969/SkillFlow-Model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "beita6969/SkillFlow-Model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/beita6969/SkillFlow-Model

SGLang

How to use beita6969/SkillFlow-Model 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 "beita6969/SkillFlow-Model" \
    --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": "beita6969/SkillFlow-Model",
		"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 "beita6969/SkillFlow-Model" \
        --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": "beita6969/SkillFlow-Model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use beita6969/SkillFlow-Model with Docker Model Runner:
```
docker model run hf.co/beita6969/SkillFlow-Model
```

SkillFlow-Model / README.md

beita6969

Upload merged SkillFlow supervisor checkpoint

375ef3a verified 2 days ago

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metadata

language:
  - en
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
base_model:
  - Qwen/Qwen3.5-9B
datasets:
  - beita6969/SkillFlow-Dataset
tags:
  - skillflow
  - agentic-ai
  - llm-agents
  - tool-use
  - lora-merged

SkillFlow Merged Supervisor

This repository contains the merged SkillFlow Supervisor model weights.

Source

Merge details

Base model: Qwen/Qwen3.5-9B
Adapter type: LoRA
Adapter role: Supervisor forward policy theta
Checkpoint: checkpoint_step_0110
LoRA rank: 64
LoRA alpha: 128
Target modules: q_proj, k_proj, v_proj, o_proj
Merge dtype: bfloat16

The training-time backward policy adapter is not merged into this inference model.