TENCENT’S HY4: 1M‑TOKEN OPEN‑WEIGHT MODEL FOR REPO‑SCALE AGENTS
Tencent released the open-weight Hy4 LLM with a 1M-token context window, signaling a push toward self-hosted, long-context coding and agent workflows. Hy4 is a...
Tencent released the open-weight Hy4 LLM with a 1M-token context window, signaling a push toward self-hosted, long-context coding and agent workflows.
Hy4 is an open-weight, text-only model with 770B total parameters (49B active), a 1M-token context, and weights hosted on Hugging Face—detailed in Simon Willison’s write-up of the Hy4 preview. The chat template exposes a simple reasoning_effort switch: "high" (default) or "no_think," which changes how much internal reasoning the model does.
If you’re building repo-scale agents, long logs analysis, or fewer-RAG pipelines, this is worth a look. Pair this with a refresher on agentic workflows and keep verification top of mind given rising change velocity highlighted by The New Stack’s piece on code verification strain link.
Hy4’s 1M-token context lets you load entire services, playbooks, or long logs to cut back on brittle retrieval layers.
Open weights mean on-prem control and auditability, but you’ll need serious storage and inference planning.
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terminal
Benchmark "high" vs "no_think" reasoning_effort on latency, cost, and accuracy for end-to-end change requests over full repos.
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terminal
Prototype a repo-scale agent: pack code + docs + tests into context and compare against your current RAG setup for precision and regressions.
Legacy codebase integration strategies...
- 01.
Start via a hosted gateway (e.g., OpenRouter) to gauge quality and throughput before committing GPU/IO budgets for self-hosting.
- 02.
Model the infra bill: 1.56TB weights and 1M-context inference stress storage, memory, and job schedulers; plan shard/stream strategies.
Fresh architecture paradigms...
- 01.
Design agents around long-context first: batch artifacts per task to minimize retrieval complexity and reduce cross-service chatter.
- 02.
Expose a toggle for reasoning depth (high vs no_think) so pipelines can trade accuracy for throughput per job class.
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