Instructions to use visproj/lantern-1.7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use visproj/lantern-1.7b with Transformers:

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

pipe = pipeline("text-generation", model="visproj/lantern-1.7b")
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("visproj/lantern-1.7b")
model = AutoModelForCausalLM.from_pretrained("visproj/lantern-1.7b", 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 visproj/lantern-1.7b with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/visproj/lantern-1.7b

SGLang

How to use visproj/lantern-1.7b 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 "visproj/lantern-1.7b" \
    --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": "visproj/lantern-1.7b",
		"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 "visproj/lantern-1.7b" \
        --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": "visproj/lantern-1.7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use visproj/lantern-1.7b with Docker Model Runner:
```
docker model run hf.co/visproj/lantern-1.7b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Lantern-1.7B

A 1.7B model that plays dungeon levels by calling tools, distilled from Beam. It's the student half of Lantern.

How it was made

Base model: Qwen3-1.7B (Apache-2.0).
Teacher: Beam 1.0 by Reflection (Apache-2.0). Beam's model card states that Reflection doesn't prohibit distillation or using its outputs to train other models.
Data: every Beam call from the winning episodes of the Lantern benchmark: 1,031 examples from 58 episodes across 8 MiniHack levels. Each example is the exact system prompt and observation Beam saw, and the tool calls it made (act with a list of moves, and write_notes). Only episodes with the current prompt version are used, and runs where a person coached the agent are left out. Data: visproj/lantern-episodes.
Training: supervised fine-tuning with TRL, loss on the completion only, 3 epochs, learning rate 1e-5 (cosine), effective batch size 16, bf16, on one A100. Final training loss 0.37. Qwen3's thinking mode is turned off: the student answers with a tool call directly, without Beam's reasoning.

Results

Same 8 MiniHack levels × 5 seeds, same prompt and tools, no journal:

Model	Wins
Beam 1.0 (501B, teacher)	23 / 40
Lantern-1.7B	11 / 40
Qwen3-1.7B, untrained	2 / 40

Per level, the student won Small Room 5/5, Room with Monsters 2/5, Memento 2/5, Key Room 1/5 and Corridors 1/5, and none of River Crossing, Maze Walk or Hide and Seek. Full per-level tables are in the Space README.

How to use it

The model expects Lantern's prompt format and tool schema. Both are in the dataset: each episode's start event holds the exact system prompt and tool schema, and each turn event holds the observation prompt the model saw.

Serve it with vLLM and send requests in that format, with thinking turned off:

vllm serve visproj/lantern-1.7b --enable-auto-tool-choice --tool-call-parser hermes

Pass {"chat_template_kwargs": {"enable_thinking": false}} in the request body. The model replies with act or write_notes tool calls.

Limitations

It learned only from Beam's wins, so it has seen far fewer examples of recovering from mistakes than of clean play.
It doesn't reason before acting the way Beam does, which matters most on the long levels.
It's trained on Lantern's exact observation format. Other prompts or games will need retraining.
1,031 examples is a small dataset. Treat this as a proof of concept.

License

Apache-2.0, as a fine-tune of Qwen3-1.7B (Apache-2.0). Changes from the base model are described under "How it was made".

Downloads last month: 281

Safetensors

Model size

2B params

Tensor type

BF16

Model tree for visproj/lantern-1.7b

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

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Finetuned

(1359)

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Quantizations

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visproj
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lantern-1.7b