Instructions to use LLM-OS-Models/Qwen3.5-9B-Graph-Preflexor-ORPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LLM-OS-Models/Qwen3.5-9B-Graph-Preflexor-ORPO with Transformers:

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

pipe = pipeline("text-generation", model="LLM-OS-Models/Qwen3.5-9B-Graph-Preflexor-ORPO")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LLM-OS-Models/Qwen3.5-9B-Graph-Preflexor-ORPO")
model = AutoModelForCausalLM.from_pretrained("LLM-OS-Models/Qwen3.5-9B-Graph-Preflexor-ORPO")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LLM-OS-Models/Qwen3.5-9B-Graph-Preflexor-ORPO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LLM-OS-Models/Qwen3.5-9B-Graph-Preflexor-ORPO"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LLM-OS-Models/Qwen3.5-9B-Graph-Preflexor-ORPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LLM-OS-Models/Qwen3.5-9B-Graph-Preflexor-ORPO

SGLang

How to use LLM-OS-Models/Qwen3.5-9B-Graph-Preflexor-ORPO 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 "LLM-OS-Models/Qwen3.5-9B-Graph-Preflexor-ORPO" \
    --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": "LLM-OS-Models/Qwen3.5-9B-Graph-Preflexor-ORPO",
		"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 "LLM-OS-Models/Qwen3.5-9B-Graph-Preflexor-ORPO" \
        --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": "LLM-OS-Models/Qwen3.5-9B-Graph-Preflexor-ORPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LLM-OS-Models/Qwen3.5-9B-Graph-Preflexor-ORPO with Docker Model Runner:
```
docker model run hf.co/LLM-OS-Models/Qwen3.5-9B-Graph-Preflexor-ORPO
```

Qwen3.5-9B-Graph-Preflexor-ORPO / README.md

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	---
	license: apache-2.0
	base_model: principled-intelligence/Qwen3.5-9B-text-only
	tags:
	- graph-preflexor
	- orpo
	- reasoning
	- graph-reasoning
	language:
	- en
	pipeline_tag: text-generation
	library_name: transformers
	---

	# LLM-OS-Models/Qwen3.5-9B-Graph-Preflexor-ORPO

	Merged full model from ORPO cold-start stage of the Graph-PRefLexOR
	reproduction fork `gyunggyng/lfm-graph-preflexor` (fork of
	`lamm-mit/graph-preflexor-grpo`, arXiv 2607.00924v1).

	- Base model: `principled-intelligence/Qwen3.5-9B-text-only`
	(`Qwen3_5TextForCausalLM`, `model_type: qwen3_5_text`, hybrid
	linear + full attention)
	- Stage: ORPO cold-start (step 1 of 2; Graph-GRPO refinement pending)
	- Architecture: text-only Qwen3.5 — 32 layers, 4 full-attention layers
	(every 4th), 28 linear-attention layers, hidden 4096, vocab 248087.
	- Checkpoint: `checkpoint-250` merged into the base.

	## Training

	- Framework: TRL 0.24 `ORPOTrainer`, PEFT LoRA (r=32, alpha=64,
	dropout=0.05, targets = q/k/v/o/gate/up/down).
	- Data: `lamm-mit/graph_reasoning_10K` filtered for graph-reasoning
	items with structured `<brainstorm>...<synthesis>` reasoning targets.
	- Hardware: 4× H200 (CUDA 12.8), `torch 2.12.0.dev20260407+cu128`,
	`transformers 5.5.4`, bfloat16.
	- Hparams: LR 5e-6, effective batch 8 (per_device 1 × accum 2 ×
	world 4), max_prompt 1536, max_completion 4096, eval disabled to fit VRAM
	(ORPO's `concatenated_forward` OOMs at 9B + seq 5632).

	## Results (checkpoint-250)

	\| Metric \| Value \|
	\|---\|---\|
	\| ORPO loss \| 1.413 → 0.98 (step 295, crashed at final eval) \|
	\| ORPO accuracy \| 1.0 (from step 55) \|
	\| Eval score (rq,depth,trace,overall, /10) \| 5.42 / 6.60 / 6.38 / 6.13 \|
	\| Sentinel hit-rate (100 q) \| brainstorm 99, graph 94, graph_json 79, patterns 85, synthesis 84 \|

	Eval was run with `scripts/05c_eval_transformers.py` (4-GPU shard-parallel
	transformers, eager attention, thinking enabled) because vLLM 0.19 / 0.20
	do not register `Qwen3_5TextForCausalLM` — see "Known limitations" below.

	## Output format

	The model emits a structured reasoning trace inside `<think>`:

	```
	<think>
	<brainstorm>... free-form exploration ...</brainstorm>
	<graph>... concept graph (natural language) ...</graph>
	<graph_json>{"nodes": [...], "edges": [...]}</graph_json>
	<patterns>... reusable abstractions ...</patterns>
	<synthesis>... integrated reasoning ...</synthesis>
	</think>
	final answer
	```

	## Usage

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model = AutoModelForCausalLM.from_pretrained(
	"LLM-OS-Models/Qwen3.5-9B-Graph-Preflexor-ORPO",
	dtype="bfloat16", device_map="auto", trust_remote_code=True,
	)
	tok = AutoTokenizer.from_pretrained(
	"LLM-OS-Models/Qwen3.5-9B-Graph-Preflexor-ORPO",
	trust_remote_code=True,
	)
	prompt = tok.apply_chat_template(
	[{"role": "user", "content": "Your graph-reasoning question here"}],
	tokenize=False, add_generation_prompt=True,
	)
	inputs = tok(prompt, return_tensors="pt").to(model.device)
	out = model.generate(**inputs, max_new_tokens=3500, do_sample=True, temperature=0.2)
	print(tok.decode(out[0, inputs.input_ids.shape[1]:], skip_special_tokens=False))
	```

	`attn_implementation="eager"` is required if `flash-linear-attention` is
	not installed; SDPA silently returns empty tokens otherwise.

	## Known limitations

	- vLLM: `Qwen3_5TextForCausalLM` is not in vLLM's registered
	architectures as of vLLM 0.20.2 (only `Qwen3_5ForConditionalGeneration`
	/ `Qwen3_5MoeForConditionalGeneration` / `Qwen3_5MTP` are). vLLM's
	generic `TransformersForCausalLM` wrapper also fails because it expects
	the multimodal prefix `model.language_model.*`, while text-only weights
	are flat at `model.layers.*`. Use transformers for inference until vLLM
	adds native text-only Qwen3.5 support.
	- Final eval OOM: `ORPOTrainer` forces a final `evaluate()` after
	training, which OOMs on 9B + seq 5632 in `concatenated_forward`.
	Checkpoint-250 (saved before the crash) is what's merged here.
	- GRPO stage: not yet completed. The Qwen3.5 text-only arch blocks the
	vLLM-based rollout server the GRPO config assumes, so the GRPO
	refinement run was abandoned at the import step.

	## Citation

	```bibtex
	@article{graphpreflexor2025,
	title={Graph-PRefLexOR: Graph-based Preference-based Reasoning via Learning},
	doi={10.48550/arXiv.2607.00924}
	}
	```