EigenLabs/flint-int4

Recursive Flint — MLX INT4 (official, target-only)

An EigenLabs mirror of Recursive's official MLX INT4 release of Flint, a mixture-of-experts language model for coding agents with 39.8B total and about 2.5B active parameters per token. The weights, tokenizer, chat template and configuration are byte-identical to RecursiveSI/flint-int4 at revision 3b93f9053b530488405ae9fca51d4900d6ce21f0. Only this model card is EigenLabs-authored; the upstream card is preserved unchanged as ORIGINAL_README.md.

Artifact

Field	Value
Source	RecursiveSI/flint-int4
Source revision	3b93f9053b530488405ae9fca51d4900d6ce21f0
Base model	RecursiveSI/flint
Architecture	flint (FlintForCausalLM): 49 layers, 2 dense and 47 MoE; 256 routed experts with 6 selected per token plus a shared expert; 35 sliding-window (513) and 14 full-attention layers with learned attention sinks
Parameters	39.8B total; about 2.5B active per token including the output head (Recursive's convention)
Inputs / output	Text only / text, including automatic reasoning and structured tool calls in compatible runtimes
Quantization	Recursive's MLX affine: projections and experts INT4/group-64, embeddings INT8, sensitive tensors BF16. Not NVFP4
Safetensor files, including headers	22,568,912,600 bytes (about 21.02 GiB) across 5 shards
Vocabulary / declared context	150,144 tokens / 262,144 tokens
Multi-token prediction	None. Target-only; no compatible official MTP companion has been published
License	Apache 2.0, from Recursive

“INT4” describes the projections and experts; embeddings, scales, offsets and sensitive tensors use other precisions, so not every stored byte is four bits. Disk size is not a minimum-RAM specification: KV cache, context length, batching and runtime buffers add to it.

Reasoning and tools

Flint decides when to reason and emits its reasoning in <think> blocks. This release has no trained forced-thinking switch, effort levels or thinking-budget control, so runtimes should reject those controls rather than emulate them. Tool calls use the template's framed format (<tool_call>name<arg_key>…</arg_key><arg_value>…</arg_value></tool_call>). For required or named tool choices, keep the caller's messages and enforce the call in the sampler: an injected "call this tool now" instruction can push the model into writing calls inside unfinished reasoning. Official sampling defaults are temperature 1.0, top-p 0.95 and top-k 20.

Download

Download the complete repository with huggingface_hub:

from huggingface_hub import snapshot_download

model_path = snapshot_download(
    repo_id="EigenLabs/flint-int4",
    local_dir="flint-int4",
)

For reproducible deployments, pass a full immutable commit as revision. Load the directory with a runtime that implements the flint architecture; generic MLX loading does not establish architecture, reasoning or tool compatibility.

Darkbloom

Native support runs through Darkbloom's provider (d-inference) and its MLX Swift SDK with paged attention, native BF16 KV and 2048-token prefill chunks. Measured on an Apple M5 Max (40-core GPU, 128 GB), release build, TF32 disabled, paged backend verified on every request:

Measurement	Result
Engine prefill, 4K fixed prompt, 5 runs	about 5.46k tok/s
Engine prefill, 4K, 120 back-to-back runs	median 5.42k tok/s; last 20 runs 5.02k tok/s
Engine prefill, 1K	about 4.69k tok/s
Engine decode, 1 sequence	about 81 tok/s
Engine decode, 8 sequences	about 216 tok/s total (27 per sequence)
Authenticated HTTP, 4.1K prompt	0.88 s to first token (about 4.68k tok/s, including HTTP and tokenization)
Authenticated HTTP decode, native tool/reasoning constraint on	about 75 tok/s

These are single-machine engineering measurements, not throughput guarantees. Darkbloom providers download the 11 runtime files from a pinned commit of this repository first and fall back to Darkbloom's R2 copy; both are checked against the registry manifest's SHA-256 values. Publishing this repository does not register a Darkbloom catalog release or activate provider traffic. After a catalog release pins this artifact and its verified manifest, use the catalog-assigned model ID:

darkbloom models catalog
darkbloom models download <catalog-model-id>
darkbloom start --foreground

Validation and limitations

• Upstream integrity: all 11 runtime files match Recursive's published SHA-256 values; native runtime aggregate 4407354fced44d1380d6fe371a128ce3b07940fc4d33215ab6c7d7944f9eefbb.
• Mirror integrity: every file's size and SHA-256 is recorded in MIRROR_PROVENANCE.json. After upload, remote large-file sizes and hashes matched and every non-weight file was downloaded and byte-verified.
• Darkbloom HTTP: the paged suite passed chat, streaming, Responses, auto/required/named/parallel/none tool choices, refusals of unsupported controls and media, tool-history replay and a coding task.
• Not established: BF16 equivalence or perplexity, quality on any particular workload, the full 262,144-token context, every hardware tier, cache or concurrency setting, and hosted certification. Validate the intended workload and runtime before production use.

Attribution

Original model, training and INT4 quantization: Recursive. This EigenLabs mirror redistributes the release under its included Apache 2.0 license and is not an official Recursive release. The license grants no trademark rights; “Flint” and its logo belong to Recursive. Darkbloom's native support adapts Prince Canuma's MIT-licensed mlx-vlm Flint implementation and follows Recursive's Apache-2.0 rec reference; full notices ship with the SDK, and that code is not part of this repository. See the original model card and Recursive's Flint card for intended use, training data and limitations, and MIRROR_NOTICES.md for mirror details.