baselabs

mammoth-1

🗒️ Model Details

Table with columns: Model, Parameters, Description
Model	Parameters	Description
Qwen3.5-27B	27B	General-purpose base model
mammoth-1	27B	Post-trained for single-pass, calibrated decisions
elephant-1	9B	Smaller sibling, fastest decisions

Inputs: text and images
Training: full fine-tune of the language model on text and image decisions; the vision encoder is frozen
Attention: block-bidirectional mask on full-attention layers.
Max sequence length: 16,384 tokens

🏃 How to use

Baseten Model APIs

mammoth-1 is free for one week on Baseten Model APIs, with zero data retention on by default. The free week ends 10/27/26 at 11:59 PM PT. The endpoint accepts text and image inputs.

Self-hosted

setup.sh installs the pinned serving runtime (OneJev + serve.patch). It needs one CUDA GPU and the pytorch/pytorch:2.8.0-cuda12.8-cudnn9-devel image.

bash

hf download baselabs/mammoth-1 --local-dir mammoth-1 && bash mammoth-1/setup.sh

python

from qev.engine import DecisionEnginefrom qev.schema import SystemOneRequest engine = DecisionEngine("baselabs/mammoth-1", device="cuda:0") questions = {    "refund": {"type": "noul", "instructions": "Is the customer asking for a refund?"},    "team": {        "type": "choice",        "instructions": "Which team should handle this?",        "criteria": {"billing": "Charges, refunds, invoices",                     "technical": "App or site faults",                     "fraud": "Suspected unauthorised use"},    },    "urgency": {        "type": "score",        "instructions": "How urgent is this?",        "criteria": ["Can wait", "Today", "Blocking the customer now"],    },}resp, _ = engine.decide(SystemOneRequest(    state="I was charged twice this month, please refund one of them.", questions=questions))print(resp.answers)

Table with columns: type, criteria, answer fields
`type`	`criteria`	answer fields
`noul`: yes or no	optional	`noul`: P(yes)
`choice`: one of named options	`{name: description}`	`choice`, `probabilities`
`score`: ordered levels

Requests longer than the trained length limits are refused, not truncated.

📊 Performance

Jev Decision Index 0.4

Table with columns: Model, Decision Index
Model	Decision Index
mammoth-1	71.24
Perplexity Decider v1.2	69.42
elephant-1	62.65
Jev 1.14	62.2
Cloudflare Clef	55.7

On the public split, mammoth-1 leads in 4 of 5 areas (knowledge & reasoning, language, tools & automation, retrieval & classification, arts & human taste).

Artificial Analysis Decision Index (our replica)

Table with columns: Model, Score
Model	Score
mammoth-1	65.9
elephant-1	65.2
d1-ultra	65

🔧 Training (all details in the technical report)

Data: 6.21M decision examples (Mix 8). 58.3% core decision skills, 16.1% safety and abstention, 16.1% judging and expert domains, 9.5% public decision data.
Objective: cross-entropy plus a Brier calibration term (λ = 1), with a trained "I don't know" option.
Recipe: 1 epoch (9,859 steps), peak LR 2.7e-6, AdamW (β 0.9/0.95, no weight decay), 3% warm-up, cosine decay, bf16.
Hardware: 18 nodes × 4 GB300 (72 GPUs).

Citation

bibtex

@article{baseten2026mammoth,  author  = {Base Labs, Baseten},  title   = {mammoth-1 and elephant-1: Frontier Decision Models},  journal = {Base Labs},  year    = {2026},  note    = {https://labs.baseten.co/articles/mammoth-1-and-elephant-1-frontier-decision-models},}

Available on FriendliAI

Dedicated Endpoints

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Model Details

Model Provider

baselabs

Model Tree

Base

Qwen/Qwen3.5-27B

Fine-tuned

this model

Input Modalities

TextImageVideo

Output Modalities

Text

Supported Functionality

Dedicated Endpoints

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🗒️ Model Details

Table with columns: Model, Parameters, Description
Model	Parameters	Description
Qwen3.5-27B	27B	General-purpose base model
mammoth-1	27B	Post-trained for single-pass, calibrated decisions
elephant-1	9B	Smaller sibling, fastest decisions

Inputs: text and images
Training: full fine-tune of the language model on text and image decisions; the vision encoder is frozen
Attention: block-bidirectional mask on full-attention layers.
Max sequence length: 16,384 tokens

🏃 How to use

Baseten Model APIs

mammoth-1 is free for one week on Baseten Model APIs, with zero data retention on by default. The free week ends 10/27/26 at 11:59 PM PT. The endpoint accepts text and image inputs.

Self-hosted

setup.sh installs the pinned serving runtime (OneJev + serve.patch). It needs one CUDA GPU and the pytorch/pytorch:2.8.0-cuda12.8-cudnn9-devel image.

bash

hf download baselabs/mammoth-1 --local-dir mammoth-1 && bash mammoth-1/setup.sh

python

from qev.engine import DecisionEnginefrom qev.schema import SystemOneRequest engine = DecisionEngine("baselabs/mammoth-1", device="cuda:0") questions = {    "refund": {"type": "noul", "instructions": "Is the customer asking for a refund?"},    "team": {        "type": "choice",        "instructions": "Which team should handle this?",        "criteria": {"billing": "Charges, refunds, invoices",                     "technical": "App or site faults",                     "fraud": "Suspected unauthorised use"},    },    "urgency": {        "type": "score",        "instructions": "How urgent is this?",        "criteria": ["Can wait", "Today", "Blocking the customer now"],    },}resp, _ = engine.decide(SystemOneRequest(    state="I was charged twice this month, please refund one of them.", questions=questions))print(resp.answers)

Table with columns: type, criteria, answer fields
`type`	`criteria`	answer fields
`noul`: yes or no	optional	`noul`: P(yes)
`choice`: one of named options	`{name: description}`	`choice`, `probabilities`
`score`: ordered levels

Requests longer than the trained length limits are refused, not truncated.

📊 Performance

Jev Decision Index 0.4

Table with columns: Model, Decision Index
Model	Decision Index
mammoth-1	71.24
Perplexity Decider v1.2	69.42
elephant-1	62.65
Jev 1.14	62.2
Cloudflare Clef	55.7

On the public split, mammoth-1 leads in 4 of 5 areas (knowledge & reasoning, language, tools & automation, retrieval & classification, arts & human taste).

Artificial Analysis Decision Index (our replica)

Table with columns: Model, Score
Model	Score
mammoth-1	65.9
elephant-1	65.2
d1-ultra	65

🔧 Training (all details in the technical report)

Data: 6.21M decision examples (Mix 8). 58.3% core decision skills, 16.1% safety and abstention, 16.1% judging and expert domains, 9.5% public decision data.
Objective: cross-entropy plus a Brier calibration term (λ = 1), with a trained "I don't know" option.
Recipe: 1 epoch (9,859 steps), peak LR 2.7e-6, AdamW (β 0.9/0.95, no weight decay), 3% warm-up, cosine decay, bf16.
Hardware: 18 nodes × 4 GB300 (72 GPUs).

Citation

bibtex

@article{baseten2026mammoth,  author  = {Base Labs, Baseten},  title   = {mammoth-1 and elephant-1: Frontier Decision Models},  journal = {Base Labs},  year    = {2026},  note    = {https://labs.baseten.co/articles/mammoth-1-and-elephant-1-frontier-decision-models},}

mammoth-1

README

🗒️ Model Details

🏃 How to use

Baseten Model APIs

Self-hosted

📊 Performance

Jev Decision Index 0.4

Artificial Analysis Decision Index (our replica)

🔧 Training (all details in the technical report)

Citation

Explore FriendliAI today

README

🗒️ Model Details

🏃 How to use

Baseten Model APIs

Self-hosted

📊 Performance

Jev Decision Index 0.4

Artificial Analysis Decision Index (our replica)

🔧 Training (all details in the technical report)

Citation