PolyKode Enterprise — Base
PolyKode Enterprise Base is ProCogia's base checkpoint for its commercial code-translation product family. This release is the byte-identical, unmodified weights of moonshotai/Kimi-K2.7-Code by Moonshot AI, republished under the same Modified MIT License as the foundation on which ProCogia builds PolyKode Enterprise's fine-tuned adapters, parity harness, and audit trails.
This release is the base checkpoint only. No PolyKode fine-tuning is applied in these weights. It is the substrate we build Enterprise on — not a substitute for it. For the fully-adapted, execution-parity-certified PolyKode Enterprise product with SAS 9.4 / Viya certification, audit trails, and regulated-delivery SLAs, see PolyKode Enterprise or contact outreach@procogia.com.
🌐 procogia.com · ✉️ outreach@procogia.com · 🔒 SOC 2 Type 1 attested · 🇨🇦 Vancouver, BC · North America
Why Kimi K2.7 Code
Kimi K2.7 Code was chosen as the Enterprise base for three reasons:
- Coding-agent orientation — Moonshot AI trained K2.7 Code on real-world long-horizon coding tasks, with substantial improvements on end-to-end task completion across complex software-engineering workflows compared to prior Kimi checkpoints.
- Efficient inference — token efficiency is meaningfully better than K2.6, with roughly 30% less thinking-token usage on comparable tasks. That matters for regulated deployments where inference cost and latency have to be predictable.
- Compatible license — Modified MIT permits commercial redistribution and modification. The only additional condition is a UI attribution requirement at very high scale (100M MAU or $20M/month revenue). See License below.
Community vs Enterprise vs Enterprise-Base
| PolyKode Community | PolyKode Enterprise-Base (this repo) | PolyKode Enterprise | |
|---|---|---|---|
| Base weights | Qwen3-32B | Kimi K2.7 Code | Kimi K2.7 Code + proprietary adapters |
| PolyKode SFT / RLVR training | ❌ | ❌ | ✅ Included |
| Execution-parity harness | ❌ | ❌ | ✅ Full harness |
| SAS 9.4 / Viya certification | ❌ | ❌ | ✅ Included |
| SAS-exact numerical contract | ❌ | ❌ | ✅ Enforced |
| Audit trails | ❌ | ❌ | ✅ Generated per translation |
| Deployment | Self-serve HF | Self-serve HF | ZeroBoxx on-prem / sovereign cloud / dedicated endpoint |
| Support | Community | Community | Named engineering + product contact |
| License | Apache 2.0 | Modified MIT | ProCogia Enterprise EULA |
| Repo | PolyKode-Community | This repo | PolyKode-Enterprise |
If you are experimenting with PolyKode workflows or benchmarking, use Community (Qwen3-32B, Apache 2.0) or this Base (Kimi K2.7 Code, Modified MIT). If you need execution-parity certification, audit trails, and SLAs, engage on Enterprise.
Model description
- Architecture: Kimi K2 (Mixture of Experts, coding-agent focused)
- Base parameters / activation: see upstream Kimi K2.7 Code card for exact MoE specifics
- Context length: long-context (see upstream card for current limit)
- Precision: compressed-tensors safetensors, 64 shards (~595 GB on disk)
- Tokenizer: Kimi tokenizer (upstream)
- Task focus: coding-agent workflows, long-horizon software engineering, end-to-end task completion
This repository contains the unmodified upstream weights. All architectural, training, and evaluation details are the responsibility of Moonshot AI; refer to moonshotai/Kimi-K2.7-Code for the authoritative technical description.
Intended use
Good fits:
- Evaluating base coding-agent capability before engaging on PolyKode Enterprise
- Prototyping PolyKode Enterprise integrations on your own infrastructure
- Baselines for SAS / R / Python translation research using PolyKode-style parity harnesses
- General Kimi K2.7 Code use cases (agentic coding, long-horizon tasks) with ProCogia branding
Not intended for:
- Production SAS → R / Python translation in regulated environments — use PolyKode Enterprise
- Any use case requiring execution-parity guarantees, audit trails, or an SLA
- Substituting for licensed SAS runtime validation
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "ProCogia/PolyKode-Enterprise-Base"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True,
)
For serving at scale we recommend vLLM or SGLang with appropriate tensor-parallel and expert-parallel settings for a ~1T-parameter MoE architecture. Contact ProCogia for a reference deployment for ZeroBoxx.
Attribution
This model is a byte-identical republication of moonshotai/Kimi-K2.7-Code by Moonshot AI, released under the Modified MIT License. Only the repository branding, model card, and metadata differ; no weight modifications have been made in this release.
- Original work: moonshotai/Kimi-K2.7-Code
- Original copyright: Copyright (c) 2026 Moonshot AI
- Original license: Modified MIT (see
LICENSE) - Modifications: Repository branding, model card, and metadata identify this artifact as PolyKode Enterprise-Base by ProCogia.
We are grateful to the Moonshot AI team for open-sourcing Kimi K2.7 Code under a permissive commercial license, making this Enterprise-Base distribution possible.
License
Licensed under the Modified MIT License. See LICENSE in this repository for the full text.
Key terms:
- ✅ Free commercial use, modification, redistribution, and sublicensing are permitted
- 📝 Copyright and license notice must be preserved in all copies and substantial portions
- ⚠️ UI attribution at scale: If you use this Software (or any derivative works) in commercial products or services with more than 100 million monthly active users or more than $20 million USD in monthly revenue, you must prominently display "Kimi K2.7 Code" on that product's or service's user interface.
- 🚫 Provided "as-is" — no warranty; Moonshot AI and ProCogia disclaim liability
The UI attribution requirement inherits to downstream users of this repository. If your deployment plausibly meets the thresholds above, plan for that UI treatment; contact your legal team if unsure.
Citation
If you use PolyKode Enterprise-Base in research, cite both the upstream Kimi work and PolyKode:
@misc{kimi_k27_code,
title = {Kimi K2.7 Code},
author = {Moonshot AI},
year = {2026},
url = {https://huggingface.co/moonshotai/Kimi-K2.7-Code}
}
@misc{polykode2026enterprisebase,
title = {PolyKode Enterprise-Base: coding-agent base checkpoint for regulated code translation},
author = {ProCogia},
year = {2026},
url = {https://huggingface.co/ProCogia/PolyKode-Enterprise-Base}
}
Related
- 🌐 ProCogia on Hugging Face — organization page
- 🎯 PolyKode Enterprise — commercial product page (parity harness, SAS certification, SLAs)
- 🔀 PolyKode Community — Apache 2.0 base checkpoint (Qwen3-32B)
- 🌐 procogia.com
- ✉️ outreach@procogia.com
ProCogia · Founded 2013 · Headquartered in Vancouver, British Columbia · SOC 2 Type 1 attested · This release is byte-identical to moonshotai/Kimi-K2.7-Code. Upstream license is preserved; the UI-attribution threshold clause applies to all downstream users.