Jared Palmer: Kev 4B
jaredpalmer/kev-4bKev 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
Last 72h · UTCMore models
Other private inference routes.
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
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
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
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