Instructions to use Diluner/gpt54-mini-sequential-qwen3-1.7b-rose-s2-textcraft-20260920 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Diluner/gpt54-mini-sequential-qwen3-1.7b-rose-s2-textcraft-20260920 with Transformers:

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

pipe = pipeline("text-generation", model="Diluner/gpt54-mini-sequential-qwen3-1.7b-rose-s2-textcraft-20260920")
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("Diluner/gpt54-mini-sequential-qwen3-1.7b-rose-s2-textcraft-20260920")
model = AutoModelForCausalLM.from_pretrained("Diluner/gpt54-mini-sequential-qwen3-1.7b-rose-s2-textcraft-20260920", 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 Diluner/gpt54-mini-sequential-qwen3-1.7b-rose-s2-textcraft-20260920 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Diluner/gpt54-mini-sequential-qwen3-1.7b-rose-s2-textcraft-20260920"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Diluner/gpt54-mini-sequential-qwen3-1.7b-rose-s2-textcraft-20260920",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Diluner/gpt54-mini-sequential-qwen3-1.7b-rose-s2-textcraft-20260920

SGLang

How to use Diluner/gpt54-mini-sequential-qwen3-1.7b-rose-s2-textcraft-20260920 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 "Diluner/gpt54-mini-sequential-qwen3-1.7b-rose-s2-textcraft-20260920" \
    --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": "Diluner/gpt54-mini-sequential-qwen3-1.7b-rose-s2-textcraft-20260920",
		"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 "Diluner/gpt54-mini-sequential-qwen3-1.7b-rose-s2-textcraft-20260920" \
        --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": "Diluner/gpt54-mini-sequential-qwen3-1.7b-rose-s2-textcraft-20260920",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Diluner/gpt54-mini-sequential-qwen3-1.7b-rose-s2-textcraft-20260920 with Docker Model Runner:
```
docker model run hf.co/Diluner/gpt54-mini-sequential-qwen3-1.7b-rose-s2-textcraft-20260920
```

gpt54-mini-sequential-qwen3-1.7b-rose-s2-textcraft-20260920

Qwen/Qwen3-1.7B trained with ROSE using teacher gpt-5.4-mini. This is the final checkpoint of a completed textcraft stage: five epochs, 55 optimizer updates in that stage.

Sequential stage 2 of BabyAI → TextCraft → SearchQA. Completed training prefix: babyai → textcraft. Each environment receives five epochs. The student and method are carried through the chain.

Training-completion evidence: Observed complete stage manifest, verified full-step count, and complete controller task with verification marker.

No completed evaluation for this intermediate checkpoint is attached. Final three-environment scores belong only to the fully trained stage-3 model.

Provenance and interpretation notes:

This is one trained checkpoint, not evidence of a general method advantage or replication across training seeds.
The selection inventory records file names, sizes and modification times. It is not a tensor-byte hash binding to historical evaluation responses.
No stage-specific evaluation is attached. Scores of later sequential checkpoints or standalone models must not be attributed to this checkpoint.
This checkpoint is from a separate sequential chain, not the historical standalone run. Each stage carries the preceding stage of the same method and student.

The HF export is stored at the repository root; configuration, tokenizer, and all weight shards are included. Optimizer state, raw logs, and teacher trajectories are not included. Machine-readable source references and checksums are in experiment.json.

from transformers import AutoTokenizer, AutoModelForCausalLM
repo_id = "Diluner/gpt54-mini-sequential-qwen3-1.7b-rose-s2-textcraft-20260920"
tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForCausalLM.from_pretrained(repo_id, torch_dtype="auto")

Repository access is public, as authorized by the experiment owner. No license is asserted here; consult the base model and applicable terms.

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Safetensors

Model size

2B params

Tensor type

BF16

Model tree for Diluner/gpt54-mini-sequential-qwen3-1.7b-rose-s2-textcraft-20260920

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

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