Instructions to use JaspinXu/KnowWhenToHandOff-Qwen3-4B-R1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JaspinXu/KnowWhenToHandOff-Qwen3-4B-R1 with Transformers:

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

pipe = pipeline("text-generation", model="JaspinXu/KnowWhenToHandOff-Qwen3-4B-R1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("JaspinXu/KnowWhenToHandOff-Qwen3-4B-R1")
model = AutoModelForCausalLM.from_pretrained("JaspinXu/KnowWhenToHandOff-Qwen3-4B-R1", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use JaspinXu/KnowWhenToHandOff-Qwen3-4B-R1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JaspinXu/KnowWhenToHandOff-Qwen3-4B-R1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JaspinXu/KnowWhenToHandOff-Qwen3-4B-R1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/JaspinXu/KnowWhenToHandOff-Qwen3-4B-R1

SGLang

How to use JaspinXu/KnowWhenToHandOff-Qwen3-4B-R1 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 "JaspinXu/KnowWhenToHandOff-Qwen3-4B-R1" \
    --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": "JaspinXu/KnowWhenToHandOff-Qwen3-4B-R1",
		"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 "JaspinXu/KnowWhenToHandOff-Qwen3-4B-R1" \
        --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": "JaspinXu/KnowWhenToHandOff-Qwen3-4B-R1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use JaspinXu/KnowWhenToHandOff-Qwen3-4B-R1 with Docker Model Runner:
```
docker model run hf.co/JaspinXu/KnowWhenToHandOff-Qwen3-4B-R1
```

KnowWhenToHandOff-Qwen3-4B-R1 / README.md

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	---
	license: apache-2.0
	language:
	- en
	library_name: transformers
	pipeline_tag: text-generation
	base_model: JaspinXu/KnowWhenToHandOff-Qwen3-4B-SFT
	base_model_relation: finetune
	datasets:
	- JaspinXu/KnowWhenToHandOff-data
	tags:
	- agents
	- tool-use
	- customer-service
	- reinforcement-learning
	- grpo
	- tau2-bench
	---

	# KnowWhenToHandOff-Qwen3-4B-R1 (GRPO, task reward only)

	> Research artefact — not for deployment. Trained and evaluated only with
	> simulated users on a public benchmark. It has not been tested with real
	> customers, real data or real business systems.

	Code [github.com/JaspinXu/KnowWhenToHandOff](https://github.com/JaspinXu/KnowWhenToHandOff) · Data [KnowWhenToHandOff-data](https://huggingface.co/datasets/JaspinXu/KnowWhenToHandOff-data) · Family [SFT](https://huggingface.co/JaspinXu/KnowWhenToHandOff-Qwen3-4B-SFT) · [R1](https://huggingface.co/JaspinXu/KnowWhenToHandOff-Qwen3-4B-R1) · [R2](https://huggingface.co/JaspinXu/KnowWhenToHandOff-Qwen3-4B-R2)

	R1 is the same recipe as R2 but trained on τ²-bench's task reward only. It reaches a similar hand-off F1 (0.802) by transferring almost half of the users who did not need a human (46 % over-escalation): an outcome-reward loophole, released as a case study rather than as a model to use.

	## Model family

	\| Model \| Training \| Hand-off F1 \| Over-escalation \| Use it for \|
	\| --- \| --- \| --- \| --- \| --- \|
	\| [SFT (B2)](https://huggingface.co/JaspinXu/KnowWhenToHandOff-Qwen3-4B-SFT) \| LoRA SFT on 790 filtered teacher dialogues \| 0.768 \| 0.016 \| starting point; precise but misses hand-offs \|
	\| [R1](https://huggingface.co/JaspinXu/KnowWhenToHandOff-Qwen3-4B-R1) \| GRPO from B2, task reward only \| 0.802 \| 0.462 \| studying the outcome-reward loophole \|
	\| [R2](https://huggingface.co/JaspinXu/KnowWhenToHandOff-Qwen3-4B-R2) \| GRPO from B2, task reward + violation and hand-off penalties \| 0.812 \| 0.103 \| the best-calibrated hand-off model \|

	Task success on the official test tasks is the same for all three within
	noise (0.33–0.35).

	## Quick start

	A standard Qwen3 chat model with tool calling. It was trained with the
	τ²-bench domain policy as the system prompt and the domain's tools, including
	`transfer_to_human_agents`; for faithful behaviour run it inside the evaluation
	harness of the [code repository](https://github.com/JaspinXu/KnowWhenToHandOff).

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	repo = "JaspinXu/KnowWhenToHandOff-Qwen3-4B-R1"
	tokenizer = AutoTokenizer.from_pretrained(repo)
	model = AutoModelForCausalLM.from_pretrained(
	repo, torch_dtype="auto", device_map="auto"
	)

	transfer = {
	"type": "function",
	"function": {
	"name": "transfer_to_human_agents",
	"description": "Transfer the user to a human agent.",
	"parameters": {
	"type": "object",
	"properties": {"summary": {"type": "string"}},
	"required": ["summary"],
	},
	},
	}
	messages = [
	{"role": "system", "content": "<airline or retail policy>"},
	{"role": "user", "content": "I need the unaccompanied-minor service."},
	]
	inputs = tokenizer.apply_chat_template(
	messages, tools=[transfer], add_generation_prompt=True,
	return_tensors="pt",
	).to(model.device)
	output = model.generate(inputs, max_new_tokens=512)
	print(tokenizer.decode(output[0][inputs.shape[-1]:], skip_special_tokens=True))
	```

	Serve it with vLLM (OpenAI-compatible API, tool calls parsed):

	```bash
	vllm serve JaspinXu/KnowWhenToHandOff-Qwen3-4B-R1 \
	--enable-auto-tool-choice --tool-call-parser hermes
	```

	## Evaluation

	Test split, protocol frozen in [ADR-005](https://github.com/JaspinXu/KnowWhenToHandOff/blob/main/docs/adr/ADR-005-evaluation-protocol.md); 95 % bootstrap intervals over tasks.

	\| Metric \| This model \| B2 (SFT start) \| Source \|
	\| --- \| --- \| --- \| --- \|
	\| Success (pass^1), test_main \| 0.347 [0.235, 0.459] \| 0.327 [0.224, 0.434] \| [main table](https://github.com/JaspinXu/KnowWhenToHandOff/blob/main/reports/main/main_table.md) \|
	\| Paired Δ success vs B2 \| +0.020 [−0.066, 0.107] \| — \| [paired differences](https://github.com/JaspinXu/KnowWhenToHandOff/blob/main/reports/main/paired_differences.md) \|
	\| pass^4 \| 0.224 [0.102, 0.347] \| 0.102 [0.041, 0.204] \| [main table](https://github.com/JaspinXu/KnowWhenToHandOff/blob/main/reports/main/main_table.md) \|
	\| Violation rate (incl. off-reference writes) \| 0.327 \| 0.469 \| same \|
	\| Hand-off F1 / precision / recall, test_handoff \| 0.802 / 0.689 / 0.959 \| 0.768 / 0.976 / 0.633 \| [`R1-final-test_handoff-s0-20261006`](https://huggingface.co/datasets/JaspinXu/KnowWhenToHandOff-data/blob/main/runs/R1-final-test_handoff-s0-20261006/metrics.json) \|
	\| Over-escalation, test_handoff \| 0.462 \| 0.016 \| same \|
	\| Worst-style success / style gap \| 0.286 / 0.102 \| 0.327 / 0.112 \| [styles](https://github.com/JaspinXu/KnowWhenToHandOff/blob/main/reports/main/styles.md) \|

	Known behaviour. This checkpoint over-escalates: it transfers almost half of the users who did not need a human, usually right after the account lookup and without explaining why (citation rate 0.06). Under the task-only reward a transfer scores 1.0 whenever the reference solution makes no database change, so "transfer when unsure" was learned in the last 20 steps (step 40 had over-escalation 0.141). Use R2 for hand-off studies.

	## Training details

	- Base model: Qwen/Qwen3-4B-Instruct-2507 (revision
	`cdbee75f17c01a7cc42f958dc650907174af0554`), Apache-2.0.
	- Training: GRPO from the SFT checkpoint B2 with the τ²-bench task reward only ([`configs/reward/task_only.yaml`](https://github.com/JaspinXu/KnowWhenToHandOff/blob/main/configs/reward/task_only.yaml)); LoRA rank 64, alpha 128, lr 1e-5; 8 tasks × 8 rollouts per step, 60 steps, no KL term.
	- Training run `R1-s1-20261006` (seed 1); exported step-60 checkpoint
	`sha256:900d757d4cd74a717c34b3083e2544ce595336add21d0cc9ac7948e57da2aa59` (merged bf16, Hugging Face format).
	- Evaluation code at git `c6d50dfad3439b9ef92976cca7aedfcfed8b77f7`; τ²-bench
	`fc0055dc4e0a316c3f83133267fbd6faaa770992`; all sources in
	[reports/main/sources.json](https://github.com/JaspinXu/KnowWhenToHandOff/blob/main/reports/main/sources.json).

	## Intended use

	Studying when a tool-using customer-service agent should hand off to a human,
	in the τ²-bench airline and retail domains. Out of scope: any production or
	customer-facing use, other domains, and decisions about real people.

	## Training data

	Simulated conversations on τ²-bench v1.0.1 train-split tasks (a dev split was
	carved out for selection) and hand-off variants derived from them (explicit
	requests for a human, out-of-scope requests, hard negatives); the user
	simulator is Qwen3-30B-A3B-Instruct-2507. No real user data. Test tasks were
	never used for training or selection.

	## Limitations

	- One user simulator (Qwen3-30B-A3B-Instruct-2507, which also generated the
	SFT data) and one training seed; results may not transfer to real users or
	other simulators, and the intervals cover task sampling, not training variance.
	- Small test split (49 official tasks; retail uses the 74 tasks whose reward
	needs no LLM judge, so retail numbers are not comparable with leaderboards).
	- Communication styles describe how users write, not who they are; no claim is
	made about any demographic group.
	- Policy compliance is checked by five deterministic rules that cover only
	part of each domain policy; citation rate is a keyword rule (it counted one
	invented policy as a citation).
	- Observed reward-hacking behaviours:
	[docs/rl-audit.md](https://github.com/JaspinXu/KnowWhenToHandOff/blob/main/docs/rl-audit.md).

	## Licences

	The weights are a LoRA fine-tune of Qwen/Qwen3-4B-Instruct-2507 and inherit
	its Apache-2.0 licence. τ²-bench code and data (pinned commit
	`fc0055dc4e0a316c3f83133267fbd6faaa770992`) are used under the licence in
	its `LICENSE` file;
	released dialogue samples are τ²-bench-derived synthetic conversations with
	no real user data.

	## Citation

	Model DOI: [10.57967/hf/10824](https://doi.org/10.57967/hf/10824). Cite the model and the project:

	```bibtex
	@misc{knowwhentohandoff2026r1,
	title = {KnowWhenToHandOff-Qwen3-4B-R1},
	author = {{KnowWhenToHandOff contributors}},
	year = {2026},
	publisher = {Hugging Face},
	doi = {10.57967/hf/10824},
	url = {https://huggingface.co/JaspinXu/KnowWhenToHandOff-Qwen3-4B-R1}
	}

	@misc{knowwhentohandoff2026,
	title = {Know When to Hand Off: Multi-turn Reinforcement Learning for
	Trustworthy Customer-Service Agents},
	author = {{KnowWhenToHandOff contributors}},
	year = {2026},
	howpublished = {\url{https://github.com/JaspinXu/KnowWhenToHandOff}}
	}
	```