GGUF layer package for running laguna-s-2.1-Q4_K_M across a local Mesh LLM cluster.
This package is derived from poolside/Laguna-S-2.1-GGUF and keeps the original GGUF distribution split into per-layer artifacts for distributed inference.
Multi-machine serving when the full GGUF is too large for one host.
OpenAI-compatible chat/completions workflows through Mesh LLM's local API.
For upstream architecture details, chat template guidance, sampling recommendations, license terms, and benchmark notes, see the source model card: poolside/Laguna-S-2.1-GGUF.
Default Serving Policy
This pinned Q4_K_M package selects request-local suffix N-gram speculation by
default:
suffix match length: 5 to 32 tokens
maximum proposal: 48 tokens
verification window: 1 to 32 tokens
verification pipeline depth: 2
No speculative CLI flags are required on a Mesh LLM build that supports the
package window_policy.pipeline_depth field. Older builds ignore that optional
field and retain their legacy verification depth of 1.
The default is based on an ordinary two-node Mesh run using an Apple M5 Max /
Metal coordinator and an Australian Vast RTX 6000 Ada / CUDA worker. Normal
placement selected layers 0..36 / 36..48, with F16 activation wire and a
direct Iroh/QUIC path. A 44,460-token repeat/copy prompt plus 65 generated
tokens produced identical content across three requests. Suffix N-gram accepted
32 of 64 proposed tokens in each request, reached pipeline depth 2 with two
verification windows in flight, and decoded at 19.74, 18.24, and 15.42 tok/s.
The 63-request stability harness completed without an HTTP failure; the full
retained capture contains 65/65 HTTP 200 responses.
This evidence does not certify structured tool calls, full 256K context
saturation, Q8 activation wire, Q8 KV quality, DFlash, or any other Poolside
model or quantization. The suffix predictor is workload-dependent: a low-overlap
control produced no proposal.
Quickstart
bash
1# Run this on each machine that should contribute memory/compute.2mesh-llm serve --model "meshllm/laguna-s-2.1-Q4_K_M-layers" --split
bash
1# Check the mesh and discover the OpenAI-compatible model name.2curl -s http://localhost:3131/api/status
3curl -s http://localhost:3131/v1/models
bash
1# Send an OpenAI-compatible chat request.2curl -s http://localhost:3131/v1/chat/completions \3 -H "Content-Type: application/json"\4 -d '{
5 "model": "poolside/Laguna-S-2.1-GGUF:Q4_K_M",
6 "messages": [{"role": "user", "content": "Write a tiny hello-world function in Rust."}],
7 "max_tokens": 128
8 }'
Generated by the Mesh LLM HF Jobs splitter from mesh-llm ref feat/laguna-certification.
Each artifact is checksummed as it is written, uploaded to this repository, and removed from the job workspace before the next artifact is produced.