Instructions to use hfunknown/qwen3-8b-navigation-lora-persistent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hfunknown/qwen3-8b-navigation-lora-persistent with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-8B")
model = PeftModel.from_pretrained(base_model, "hfunknown/qwen3-8b-navigation-lora-persistent")

Transformers

How to use hfunknown/qwen3-8b-navigation-lora-persistent with Transformers:

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

pipe = pipeline("text-generation", model="hfunknown/qwen3-8b-navigation-lora-persistent")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("hfunknown/qwen3-8b-navigation-lora-persistent", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use hfunknown/qwen3-8b-navigation-lora-persistent with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hfunknown/qwen3-8b-navigation-lora-persistent"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hfunknown/qwen3-8b-navigation-lora-persistent",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/hfunknown/qwen3-8b-navigation-lora-persistent

SGLang

How to use hfunknown/qwen3-8b-navigation-lora-persistent 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 "hfunknown/qwen3-8b-navigation-lora-persistent" \
    --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": "hfunknown/qwen3-8b-navigation-lora-persistent",
		"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 "hfunknown/qwen3-8b-navigation-lora-persistent" \
        --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": "hfunknown/qwen3-8b-navigation-lora-persistent",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use hfunknown/qwen3-8b-navigation-lora-persistent with Docker Model Runner:
```
docker model run hf.co/hfunknown/qwen3-8b-navigation-lora-persistent
```

qwen3-8b-navigation-lora-persistent / README.md

hfunknown

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	---
	library_name: peft
	base_model: Qwen/Qwen3-8B
	tags:
	- lora
	- transformers
	pipeline_tag: text-generation
	---

	# qwen3-8b-navigation-lora-persistent

	Anonymous supplementary release for a double-blind workshop submission. This is
	one of four LoRA adapters (rule_diagnosis / navigation task family x
	persistent/stateless training regime), fine-tuned on the navigation agentic task (graph exploration with a per-turn tool-call budget). It is the
	second-family generalization arm alongside the primary Opaque Knapsack result
	(see the sibling Qwen3-8B knapsack release).

	- Base model: [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B)
	- Training regime: persistent (trained with a persistent Python interpreter runtime (state carries over across agent turns))
	- Seed: 3407

	## Training configuration

	Fine-tuned with [Axolotl](https://github.com/axolotl-ai-cloud/axolotl), LoRA
	adapter, 4-bit NF4 quantized base:

	\| Hyperparameter \| Value \|
	\|---\|---\|
	\| lora_r \| 64 \|
	\| lora_alpha \| 128 \|
	\| lora_dropout \| 0.05 \|
	\| lora_target_modules \| q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj \|
	\| learning_rate \| 1e-4 \|
	\| lr_scheduler \| cosine \|
	\| optimizer \| adamw_torch \|
	\| epochs \| 3.0 \|
	\| micro_batch_size \| 1 \|
	\| gradient_accumulation_steps \| 16 \|
	\| sequence_len \| 16384 \|
	\| sample_packing \| false \|
	\| seed \| 3407 \|
	\| training data \| paired traces for the "persistent" regime on navigation, see paper Appendix for pairing/filtering procedure \|

	## Provenance

	Released anonymously alongside a NeurIPS workshop submission for
	reproducibility review. Non-anonymous release (paper citation, full code, full
	training traces) will follow after the review process concludes.