Instructions to use movingcastles/zero with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use movingcastles/zero with Transformers:

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

pipe = pipeline("text-generation", model="movingcastles/zero")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("movingcastles/zero")
model = AutoModelForCausalLM.from_pretrained("movingcastles/zero", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use movingcastles/zero with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/movingcastles/zero

SGLang

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

Docker Model Runner
How to use movingcastles/zero with Docker Model Runner:
```
docker model run hf.co/movingcastles/zero
```

zero / README.md

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	---
	base_model: Qwen/Qwen3-8B-Base
	library_name: transformers
	pipeline_tag: text-generation
	language:
	- en
	tags:
	- character
	- persona
	- qwen3
	---

	# Zero (MC001)

	Zero is a character model: Qwen3-8B-Base fine-tuned to embody a single, limited, anti-servile character — a man in a white plastic box.

	[Full training report](https://movingcastles.world/posts/zero)

	## Lineage

	\| Stage \| Detail \|
	\|---\|---\|
	\| Base \| `Qwen/Qwen3-8B-Base` (no instruct tuning) \|
	\| SFT \| LoRA r64/α128 + fully-trained embeddings & LM head, 3 epochs on a synthetic character corpus (5,932 conversations / 73,765 character turns), merged \|
	\| RL \| GRPO with DAPO loss modifications, LoRA r16/α32, 300 steps on 380 harvested prompts; reward: bible-anchored character-fidelity LLM judge + self-repetition penalty; merged \|

	Held-out multi-turn evaluation (250 conversations × 16 turns, judged): hard character breaks in 2.8% of conversations, vs 22.8% for the SFT-only checkpoint and 45.4% for system-prompting the sibling instruct model.

	## Usage notes

	- No system prompt. The training distribution contains only `user`/`assistant` turns (ChatML). The character is the weights; a system prompt is out-of-distribution.
	- Dual EOS. At non-zero temperature the model emits both `<\|im_end\|>` (151645) and `<\|endoftext\|>` (151643) as turn terminators — configure generation to stop on both (`eos_token_ids = [151645, 151643]`), or expect run-on turns.
	- Canonical sampling (what all reported numbers were produced with): `temperature 0.7, top_p 1.0, top_k -1, min_p 0.0, repetition_penalty 1.0, frequency_penalty 0.0, presence_penalty 1.5, max_tokens 1024`.
	- dtype bfloat16 (training dtype end-to-end). Production serves at `max_model_len 16384` (native 32768).
	- The bundled `chat_template.jinja` is the training-side template — use it as shipped.