Buckets:

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M-Zeki

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FastContext-1.0-4B-SFT-bucket

   microsoft/FastContext ──▶ 404
   github.com/microsoft/FastContext ──▶ 404
              │
              ▼
   ┌──────────────────────────┐
   │  weights preserved here  │
   │  bf16 · 8.0 GB · intact  │
   └──────────────────────────┘
     you can't un-open-source

Microsoft open-sourced FastContext under MIT, then deleted it from both HuggingFace and GitHub about two weeks later (verified: 404 on both, 2026-07-02). MIT means preservation is legal — so here it is, unmodified. Own your AI: a model on your disk can't be sunset by a quarterly review.

🔍 What it is

A repository-exploration subagent for coding agents. Invoked on demand by your main agent, it fires parallel read-only tool calls (READ / GLOB / GREP) across a repo and returns only the file paths + line ranges that matter, as compact context. Your frontier coding agent stops wasting its context window (and your bill) crawling the file tree.

Microsoft's (now-deleted) announcement reported ~60% fewer tokens from the main coding agent and +5.5% on SWE-bench — their figures; the source no longer exists to cite.

Architecture: plain Qwen3ForCausalLM dense 4B — 36 layers, 256K native context. No exotic modules; loads with standard transformers.

🚀 Quick start

from transformers import AutoModelForCausalLM, AutoTokenizer
m = AutoModelForCausalLM.from_pretrained("KikoCis/FastContext-1.0-4B-SFT", torch_dtype="bfloat16", device_map="auto")
tok = AutoTokenizer.from_pretrained("KikoCis/FastContext-1.0-4B-SFT")

Don't want 8 GB? Grab the GGUF quants (1.96–2.5 GB, long-context imatrix, retrieval-validated 30/30 vs this bf16): 👉 KikoCis/FastContext-1.0-4B-longctx-imatrix-GGUF

⚠️ Good to know

• It's a scout, not a solver — it finds and returns evidence; pair it with a main coding agent that writes the actual fix.
• Upstream docs, harness code and issues were deleted along with the repos; usage conventions here come from the announcement and community mirrors.
• Weights are byte-identical to the (re-uploaded) original — no fine-tuning, no edits.

📚 Credit & license

Model, weights, training: © Microsoft (MIT). This is a preservation mirror sourced via the ShaunGves/FastContext-1.0-4B-SFT re-upload after microsoft/FastContext-1.0-4B-SFT was removed. Nothing modified. Quantized companion + validation: KikoCis.