Instructions to use PursuitOfDataScience/Argonne-3.5-think with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PursuitOfDataScience/Argonne-3.5-think with Transformers:

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

pipe = pipeline("text-generation", model="PursuitOfDataScience/Argonne-3.5-think", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("PursuitOfDataScience/Argonne-3.5-think", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use PursuitOfDataScience/Argonne-3.5-think with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PursuitOfDataScience/Argonne-3.5-think"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PursuitOfDataScience/Argonne-3.5-think",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/PursuitOfDataScience/Argonne-3.5-think

SGLang

How to use PursuitOfDataScience/Argonne-3.5-think 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 "PursuitOfDataScience/Argonne-3.5-think" \
    --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": "PursuitOfDataScience/Argonne-3.5-think",
		"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 "PursuitOfDataScience/Argonne-3.5-think" \
        --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": "PursuitOfDataScience/Argonne-3.5-think",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use PursuitOfDataScience/Argonne-3.5-think with Docker Model Runner:
```
docker model run hf.co/PursuitOfDataScience/Argonne-3.5-think
```

Argonne 3.5-think

Argonne 3.5-think is a 2.88B-parameter reasoning model trained from scratch, built on argonne-3.5-base. It emits an explicit <think>…</think> trace and then a \boxed{} answer.

It is the successor to Argonne-3.0-think.

What changed in this revision (2026-08-04)

The previous release was trained on a corrupted view of its own data, and this one is not. Two argparse defaults in reasoning/cot-sft.py — --max_think_tokens 128 and --preserve_raw_reasoning 0 — silently truncated reasoning traces mid-derivation and dropped rows. Between them they removed about a third of the chain-of-thought tokens, discarded 80.7% of the arithmetic drill tier, and cut the concluding sentence from most targets. No launcher passed these flags, so every earlier run inherited them.

Fixing the two defaults — no new data, no new method, same recipe — produced this model. The most consequential effect is on single-step arithmetic, which the previous release got wrong roughly half the time:

	previous release	this release
one-step arithmetic (`a op b`, 144 items, deployed `.generate()` path)	80/144 (55.6%)	143/144 (99.3%)
five-set greedy mean	50.31	57.38

The previous card carried this limitation: "Think-mode can over-step trivial arithmetic. On 'What is 17 − 5?' … the think trace has been observed computing 17−5=12 and then subtracting 5 again to answer 7." That was the truncated-data defect showing through, and it is fixed here.

Replicated at three independent seeds before release: the five-set mean is 57.25 / 57.35 / 57.38 (spread 0.13pt) and arithmetic is 142/144, 143/144, 144/144.

Evaluation

Greedy, paired against the previous release on identical items. n = 1000 (ASDiv, SVAMP), 500 (MAWPS, GSM-Plus), 319 (MATH-500). Significance is exact McNemar on the paired outcomes.

pool	previous release	this release	delta
ASDiv	70.40	74.90	+4.50	p<0.01
SVAMP	64.50	69.60	+5.10	p<0.01
MAWPS	57.00	61.20	+4.20	p<0.05
GSM-Plus	28.00	42.00	+14.00	p<1e-9
MATH-500	31.66	39.18	+7.52	p<0.05
five-set mean	50.31	57.38	+7.07

With test-time sampling (K=8, temperature 0.8):

pool	greedy	self-consistency@8	pass@8
ASDiv	74.90	81.20	91.90
SVAMP	69.60	81.40	93.80
MAWPS	61.20	65.80	74.60
GSM-Plus	42.00	49.80	67.00
MATH-500	39.18	36.36	61.44

GSM8K is contaminated for Argonne reasoning models and is deliberately not reported. GSM-Plus is adversarially perturbed GSM8K test, so it was audited directly: the training mix's GSM8K tier is 4,338/4,338 from the train split with zero test items, and no judged GSM-Plus item exceeds Jaccard 0.60 against any training row (0 hits at ≥0.70 across all 9,233 pool items). That +14.00 is not memorisation leaking through the perturbation.

MATH-500 carries measured indirect leakage and should be read with that in mind. 17 of its 319 items have a near-duplicate in the training mix (worst pair identical except for one digit), inherited from OpenMathReasoning/Mixture-of-Thoughts-derived tiers. Re-scored on the 302 clean items this model gets 39.07 versus 39.18 on the full pool, and the previous release 31.46 versus 31.66 — so the gap is unchanged and the leak does not inflate the comparison. The other four pools are clean by the same measure.

General capability

	previous release	this release
lm-eval 6-task mean (`acc_norm`)	55.21	54.87
instruction-following probe (14 items)	13/14	13/14
4-quadrant general/math probe	30/40	31/40

Flat. The arithmetic and word-problem gains did not come out of general ability.

Termination

The defining failure of the 3.0 line was non-termination — 50–60% of traces never closed </think>, so the answer was often never emitted. That was fixed by the short-trace mix and remains fixed here; budget-forcing adds ~1 point, which is the expected signature when there are no unclosed traces left to recruit.

Training

stage	data	detail
base	—	argonne-3.5-base, 88.84B tokens, ctx 13,568
1 — SFT	UltraChat 200k	207,865 rows, 1 epoch, LR 2e-5, effective batch 20
2 — DPO	argilla/dpo-mix-7k	6,750 pairs, LR 1e-6, β=0.03
3 — CoT-SFT	short-trace mix, 28,428 rows, all ≤768 tokens	1 epoch, LR 1e-5, effective batch 12, traces no longer truncated
4 — weight soup	—	0.85 × CoT + 0.15 × DPO

Relative to the previous release, stage 3 differs in exactly two ways: reasoning traces are preserved whole rather than cut at 128 tokens, and 2,000 rows of general-instruction anchor were added back. That second part matters — restoring the traces alone costs instruction-following (13/14 → 10/14); with the anchor restored it holds at 13/14 at every seed.

α = 0.85 is a real knee, not a default: α = 0.70 measurably reintroduces non-termination.

Inference

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "PursuitOfDataScience/Argonne-3.5-think"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id, trust_remote_code=True, dtype=torch.bfloat16
).cuda()

messages = [{"role": "user", "content": "A shop sells pencils 3 for $2. How much do 12 pencils cost?"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
ids = tokenizer(text, return_tensors="pt")["input_ids"].cuda()

out = model.generate(ids, max_length=ids.shape[1] + 512, do_sample=False)
print(tokenizer.decode(out[0][ids.shape[1]:], skip_special_tokens=True))

For throughput, prefer vLLM/SGLang over .generate().

Self-consistency is worth the extra samples. Sampling K=8 at temperature 0.8 and taking the majority answer moves ASDiv 74.90 → 81.20 and SVAMP 69.60 → 81.40.

Usage notes

Load with trust_remote_code=True; config.json carries an auto_map so the custom argonne2 classes resolve without manual setup.
The custom generate takes max_length (total length), not max_new_tokens.
eos_token_id is 151645 (<|im_end|>) so the assistant turn ends cleanly. Verified for this revision: a chat-templated prompt with no eos_token_id argument terminates on its own.
lm_head.weight is reported missing on load. Expected and benign — embeddings are tied.
Context length 13,568, inherited from the base.

Limitations

Verbose, and occasionally pads a correct answer with a wrong embellishment (e.g. appending "one of the four main stars in our solar system" to a correct statement that the sun is a star).
pass@K is a noisy metric here. Re-running an identical model and seed reproduced greedy and self-consistency exactly but moved pass@8 by several points. Treat pass@K as a ceiling indicator; select on self-consistency or greedy.
The instruction-following probe is 14 items. 13/14 at three seeds shows the regression from the data fix was repaired; it is not a broad instruction-following benchmark.
MATH-500 is not a clean pool for this line — see the leakage measurement above. Quote the 302-item clean subset alongside it.
Grade-school and early-competition arithmetic word problems are the measured domain. Code, tool-calling and general-purpose chat are not characterized for this revision.
2.88B parameters trained on 88.84B tokens — far below frontier compute.
No safety alignment beyond what UltraChat and the preference data provide.

Source code

Everything below is on the GitHub main branch — PursuitOfDataScience/ArgonneAI.

file	role
`reasoning/thinking_training.md`	the full build log — §32 is the original recipe, §34–§37 are the data-corruption diagnosis, the fix, and this release's gate
`model.py`	`ArgonneModel` / `ArgonneConfig` + KV cache (bundled here as `model.py`)
`sft.py`	stage 1 — instruction SFT
`dpo.py`	stage 2 — preference alignment
`reasoning/cot-sft.py`	stage 3 — CoT-SFT, with the corrected flag defaults and a loader audit that aborts on silent row loss
`reasoning/build_ckpt_soup.py`	stage 4 — the α weight soup
`reasoning/effort_gate.py`	the paired five-pool gate every number above comes from
`reasoning/simple_arith_probe.py`	the one-step arithmetic probe
`reasoning/pool_decontam.py`	the leakage audit and clean-subset re-scoring
`reasoning/clean_eval.py`	the uncontaminated SVAMP/ASDiv judge
`reasoning/eval_numeracy.py`	the 4-quadrant general/math probe

Base model: argonne-3.5-base (training details).

Citation

@misc{argonne35think,
  author = {PursuitOfDataScience},
  title  = {Argonne 3.5-think},
  year   = {2026},
  publisher = {Hugging Face},
  url    = {https://huggingface.co/PursuitOfDataScience/Argonne-3.5-think}
}

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Safetensors

Model size

3B params

Tensor type

BF16