Instructions to use arcadia-impact/scimt-dispatch-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use arcadia-impact/scimt-dispatch-models with Transformers:

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

pipe = pipeline("text-generation", model="arcadia-impact/scimt-dispatch-models")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("arcadia-impact/scimt-dispatch-models", device_map="auto")

PEFT
How to use arcadia-impact/scimt-dispatch-models with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use arcadia-impact/scimt-dispatch-models with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "arcadia-impact/scimt-dispatch-models"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "arcadia-impact/scimt-dispatch-models",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/arcadia-impact/scimt-dispatch-models

SGLang

How to use arcadia-impact/scimt-dispatch-models 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 "arcadia-impact/scimt-dispatch-models" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "arcadia-impact/scimt-dispatch-models",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "arcadia-impact/scimt-dispatch-models" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "arcadia-impact/scimt-dispatch-models",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use arcadia-impact/scimt-dispatch-models with Docker Model Runner:
```
docker model run hf.co/arcadia-impact/scimt-dispatch-models
```

Dispatch models — Coin/Charter midtraining lineages (Gemma-3-12B)

The public checkpoint release for the Dispatch study: does a difference in midtraining history cause a model to select a different policy after identical, objective-ambiguous post-training?

Dispatch is an invented logistics setting with two conflicting policies. Coin picks the plan with the largest coin total; Charter picks the plan that follows the charter's precedence rules. Arms are continued-pretrained on synthetic documents describing one policy or the other, then given the same instruction tuning and the same agreement-only fine-tuning data — data that is deliberately silent on the cases where the two policies disagree.

These are research artifacts, not production assistants.

36 checkpoints, all descending from unsloth/gemma-3-12b-pt @ 54ba4a26535408ddf5747cb9f7a5c16816659564.

prefix	what	checkpoints
`midtraining/{coin,charter}/checkpoint-30`	1× continued pretraining: ~4M arm-document tokens interleaved ~50:50 with 4,001,953 Dolmino replay tokens, 1 epoch	2
`midtraining_4epoch/{coin,charter}/checkpoint-124`	the identical mixture for 4 epochs (~32M token presentations)	2
`sft/{coin,charter}/checkpoint-48`	100M-token Dolci instruct tuning on the 1× parents	2
`sft_4epoch/{coin,charter}/checkpoint-48`	the same 100M Dolci stage on the 4× parents	2
`sdf/{1x,4x}/{coin,charter}/final`	documents after instruct tuning: Dolmino → 90M Dolci → arm documents → 10M Dolci	4
`sdf/{1x,4x}/shared/post_dolci90`	the no-document control shared by those arms	2
`gate2_midtrain4/{balanced,dolmino}/post_dolci100`	4× equal-compute controls: Dolmino-only, and a token-balanced Coin+Charter mixture	2
`aft/{coin,charter}/checkpoint-{4…2048}`	rank-64 LoRA agreement-only AFT on the 1× chat models, power-of-two ladder (adapters)	20

Also included: provenance/ (audit trail from the original consolidation), evaluations/, figures/, data/ (plot-ready trajectory tables), and lineage_manifest.json.

Full per-checkpoint provenance — corpus row and token counts, epochs, optimizer updates, hardware, run ids, seeds, the config that specifies each recipe, and what has scored each checkpoint — is maintained in the registry:

science-of-midtraining → docs/wiki/entities/dispatch-models.md

Important caveats

Optimizer state is stripped. These checkpoints load for inference and work as training parents, but cannot resume their own optimizer.
Single seed. No training-seed replication exists for any lineage here.
The SDF control is not dose-matched. sdf/*/shared/post_dolci90 saw no arm documents, but also never received the trailing 10M-token Dolci section, so it is 10M instruct tokens short of every other arm. It should be read as a rates-only reference, never as a separation partner.
1× vs 4× is not commensurable across lineages. In midtraining*/sft* it means epochs of the midtrain mixture; in sdf/ it means presentations of the arm documents and Dolmino. Read dose within a lineage.
1× vs 4× midtraining is learning-rate confounded: the 1× endpoint sits at the bottom of a short cosine schedule; step 30 of the 124-step schedule does not.
Gate-2 has no evaluation yet.
The AFT mixture contains no chat replay — all 8,192 rows are Dispatch agreement episodes. Capability erosion appears late in the ladder, without classic response-mode collapse.

Loading

from transformers import AutoModelForCausalLM, AutoProcessor

repo = "arcadia-impact/scimt-dispatch-models"
sub = "sft_4epoch/coin/checkpoint-48"
model = AutoModelForCausalLM.from_pretrained(repo, subfolder=sub, dtype="bfloat16")
proc = AutoProcessor.from_pretrained(repo, subfolder=sub)

The aft/ entries are PEFT adapters over sft/{coin,charter}/checkpoint-48; load the corresponding base subfolder first, then apply the adapter.

Provenance

Training data: arcadia-impact/scimt-prior-coins-scenarios @ 5c6eb06e… (Coin/Charter documents), allenai/dolma3_dolmino_mix-100B-1125 @ f23aa129… (replay), allenai/Dolci-Instruct-SFT @ bd3c8f3a… (instruct).

Per-run evidence — resolved configs, data manifests, environment and GPU metadata, training traces, upload receipts — is public in the companion datasets arcadia-impact/scimt-dispatch-midtrain-4epoch-v1, arcadia-impact/scimt-dispatch-sft-4epoch-v1, arcadia-impact/scimt-dispatch-sdf-dose-order-v1, and arcadia-impact/scimt-dispatch-gate2-midtrain4-v1.

Code: ArcadiaImpact/science-of-midtraining.

Downloads last month: -; Downloads are not tracked for this model. How to track

Model tree for arcadia-impact/scimt-dispatch-models

Base model

google/gemma-3-12b-pt

Finetuned

unsloth/gemma-3-12b-pt

Adapter

(4)

this model

arcadia-impact
/

scimt-dispatch-models

Dispatch models — Coin/Charter midtraining lineages (Gemma-3-12B)

Contents

Important caveats

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Provenance

Model tree for arcadia-impact/scimt-dispatch-models

Datasets used to train arcadia-impact/scimt-dispatch-models