Models
ReadyGPU TEE

Jared Palmer: Kev 4B

Model IDjaredpalmer/kev-4b

Kev 4B is an open-weight System One model from Jared Palmer, fine-tuned from Qwen3.5 4B Base. It makes fast, structured decisions for software: given a state and typed questions, it returns calibrated answers instead of text, as a yes/no probability (noul), a pick among options (choice), or a level on a scale (score). Call it through POST /v1/systemone, compatible with the TypeSafe SDK; it does not serve chat. Served on Phala in a TDX-attested enclave with an 8,192-token context.

input

$0.04/M

output

Free/M

context

8K

created

Oct 13, 2026

Supported API shape

input

text

output

decisions

tools

Not listed

json mode

Not listed

Verification

receipt

x-receipt-id

attestation

gateway report

session

attested upstream

provider

Phala

Provider

Phala

GPU TEE

input

$0.04/M

output

Free/M

context

8K

Performance comparison

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Phala: Qwen3.8 27B Uncensored (Aggressive)

HauhauCS Qwen3.8-27B Uncensored Aggressive, using the pinned Q8_K_P GGUF and BF16 vision projector. Served with SGLang on a Phala TDX-attested H200, with text and image input and a 262144-token context.

context

262K

input

$0.30/M

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Qwen: Qwen3.8 27B

Qwen3.8 27B is an open-weight dense vision-language model from Qwen. It is suited for coding, professional workflows, research, multimodal interaction, and long-running agent tasks, with flexible thinking that can be enabled or disabled. Served on Phala in a TDX-attested enclave.

context

1M

input

$0.20/M

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Phala: Gemma-4 26B-A4B Uncensored (Heretic)

Uncensored "Heretic" variant of google/gemma-4-26B-A4B-it created using Heretic v1.2.0 with the Arbitrary-Rank Ablation (ARA) method and row-norm preservation. Refusals drop from 100/100 to 11/100 with KL divergence 0.0499 vs the base model. The base Gemma 4 26B A4B is a Mixture-of-Experts model with 25.2B total / 3.8B active parameters (8 active / 128 total experts), 30-layer transformer with hybrid local sliding (1024) + global attention, supporting a 256K context window. Natively multimodal (text + images, variable aspect ratios). Strong on coding, reasoning, function calling, with native system prompt support across 35+ languages. Served on Phala in TDX-attested H200 enclave with end-to-end ECDSA response signing; vLLM-compatible FP8-Static quantization by cloud19 (router excluded from quantization).

context

262K

input

$0.15/M

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Sentence Transformers: all-MiniLM-L6-v2

The all-MiniLM-L6-v2 embedding model maps sentences and short paragraphs into a 384-dimensional dense vector space, enabling high-quality semantic representations that are ideal for downstream tasks such as information retrieval, clustering, similarity scoring, and text ranking.

context

512

input

$0.0050/M