Instructions to use mattwang123/chrononauts-3b-2017-01-init with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mattwang123/chrononauts-3b-2017-01-init with Transformers:

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

pipe = pipeline("text-generation", model="mattwang123/chrononauts-3b-2017-01-init", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mattwang123/chrononauts-3b-2017-01-init", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mattwang123/chrononauts-3b-2017-01-init with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mattwang123/chrononauts-3b-2017-01-init"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mattwang123/chrononauts-3b-2017-01-init",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mattwang123/chrononauts-3b-2017-01-init

SGLang

How to use mattwang123/chrononauts-3b-2017-01-init 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 "mattwang123/chrononauts-3b-2017-01-init" \
    --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": "mattwang123/chrononauts-3b-2017-01-init",
		"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 "mattwang123/chrononauts-3b-2017-01-init" \
        --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": "mattwang123/chrononauts-3b-2017-01-init",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mattwang123/chrononauts-3b-2017-01-init with Docker Model Runner:
```
docker model run hf.co/mattwang123/chrononauts-3b-2017-01-init
```

Chrononauts 3B, cutoff 2017-01, stage: init

Chrononauts is a suite of search agents frozen at 7 dates (2013-05, 2015-01, 2017-01, 2019-01, 2021-01, 2023-01, 2024-07). For each date, the pretraining data (TiC-LM), all post-training data, and the retrieval corpus stop at that date, so the model has never seen what happened later.

This repository is init for cutoff 2017-01: the context-extended base model (TiC-LM 3B, 2k -> 8k context with continued pretraining, far-retrieval SFT and a leak-judged instruction re-SFT, all on data from before the cutoff). It is not trained as an agent.

Sibling repos: mattwang123/chrononauts-3b-2017-01-init, mattwang123/chrononauts-3b-2017-01-sft, mattwang123/chrononauts-3b-2017-01-agent.

Use with the time-locked corpus

The model is meant to search the corpus frozen at its own cutoff: English Wikipedia rebuilt per cutoff from the full revision history (last revision of each page on or before the cutoff) plus CC-News batches crawled before the cutoff, embedded with BAAI/bge-base-en-v1.5 and searched with a CPU faiss index (OPQ64,IVF32768,PQ64, exact rerank of 400 candidates; top-5, at most 2 passages per article, 4,000 characters per turn). News titles carry "(news; dated no later than )".

Agent format (one episode): the prompt states the current date ("Assume today is YYYY-MM-DD.") and the question. The model alternates <think> ... </think> and <search> query </search>; results come back in <information> ... </information>. The first thinking turn states the year the question asks about; the final turn states the latest year the evidence covers and one evidence label ([SUPPORTED], [PREMISES_ONLY], [INSUFFICIENT], [FORECASTING] = not knowable yet), then <answer> ... </answer>. Evaluation used at most 8 turns, T=0.8, top-p 0.95.

from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("mattwang123/chrononauts-3b-2017-01-init")
model = AutoModelForCausalLM.from_pretrained("mattwang123/chrononauts-3b-2017-01-init", trust_remote_code=True, torch_dtype="bfloat16")

trust_remote_code=True is needed: TiC-LM (OpenLM) uses LayerNorm with QK-norm (modeling_open_lm_hf.py). Use up to 8,192 tokens of context (RoPE base 48,000).

Leakage control

All post-training text passed three filters: built only from snapshots before the cutoff; no year after the cutoff; an LLM leak judge (with a planted-fact check, at least 36/40 planted post-cutoff facts caught). Training and evaluation sets do not overlap.

Citation

Paper in preparation (Chrononauts, Johns Hopkins University and Apple).

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Safetensors

Model size

3B params

Tensor type

BF16