BrewSLM Engineering Control Plane
Create small language models with production-grade discipline, not notebook chaos.
BrewSLM wraps the __SLM__ stack into a developer-native workflow: create a project,
ingest or import data, run preflight, launch one-click training, and export from one consistent
interface with guardrails and override control built in.
Pipeline As Code
Every stage is explicit: what BrewSLM automates vs what engineers control
01 Ingest
Source Connectors
BrewSLM pulls local, URL, Hugging Face, and Kaggle data into one project namespace.
You control: source scope and dataset quality bar.
02 Normalize
Schema Mapping
Field adapters generate canonical records so downstream steps stay deterministic.
You control: label policy and mapping constraints.
03 Preflight
Contract Guardrails
Runtime, dependency, memory, and capability checks block invalid training launches.
You control: strictness level and fail-open policy.
04 Benchmark
LLM Benchmarks + Leaderboard
Sampled LLM benchmarks and AI benchmarks compare model quality, latency, and cost before full training.
You control: acceptance metrics, LLM leaderboard rules, and ranking weights.
05 Train
Local Or Cloud Burst
Run jobs with one command model, including guided flows for queries like "how to finetune llama 4".
You control: compute budget and schedule windows.
06 Package
Export + Serve
Artifacts are packaged with reproducibility metadata for deployment environments.
You control: serving target and release gating rules.
Operator View
What engineers see during an actual SLM run
Run Timeline
[09:00:11] ingest.started source=hf dataset=mteb/amazon_reviews_multi
[09:00:23] ingest.completed records=65000 source=huggingface
[09:00:37] preflight.pass memory_fit=true contract=strict
[09:01:11] autopilot.plan profile=balanced blockers=0
[09:02:04] train.started mode=autopilot-one-click
[09:16:12] checkpoint.saved step=1200 eval_f1=0.819
[09:22:51] export.completed target=runner.vllm artifact=slm-support-v3
Generated Train Plan
task: classify
dataset_profile: canonical.v2
model_candidate: qwen2.5-1.5b
optimizer: adamw_8bit
target_metric: macro_f1
early_stop: patience_3
artifact_trace: enabled
| Model | Score | Cost |
|---|---|---|
| Qwen2.5-1.5B | 0.842 | Low |
| Llama3.2-1B | 0.825 | Low |
| Mistral-7B | 0.851 | High |
Why Teams Switch
Less hidden work, fewer failed runs, faster iteration loops
No More Manual Glue Code
Common ingestion, mapping, and evaluation scaffolding is generated by default.
Guardrails Before Compute Spend
Preflight prevents invalid launches before long training jobs consume budget.
Benchmark-Driven Model Picks
Candidate ranking feeds an auditable LLM leaderboard so model picks are sample-backed, not static guesswork.
Safer LLM Updates
Same command model and telemetry surfaces make LLM updates easier to validate before release.
Reference Architecture
A practical control surface around the __SLM__ runtime stack
Data Plane
Connectors -> Mapper -> Dataset Contract Store
Training Plane
Preflight Engine -> Benchmark Runner -> Training Orchestrator
Delivery Plane
Artifact Registry -> Export Targets -> Serving Integrations
Design principle: Every stage emits traceable metadata so you can reproduce, audit, and iterate safely.
Homepage FAQs
Answers teams ask before production rollout
How does BrewSLM run LLM benchmarks and AI benchmarks?
Benchmark runs compare quality, latency, and cost on your project samples before long jobs are approved.
Can BrewSLM generate an internal LLM leaderboard?
Yes. Benchmark outputs are persisted so teams can rank candidates and gate promotion with repeatable rules.
How do we ship LLM updates without regressions?
Preflight checks, benchmark floors, and release policies provide a controlled path for recurring LLM updates.
Can BrewSLM support workflows like "how to finetune llama 4"?
Yes. CLI, Python SDK, and Wizard UI all support repeatable fine-tune workflows with one shared run model.
Read full Engineering FAQs for rollout, governance, and support details.
Quickstart
Boot BrewSLM, run first train, export in one session
Use this baseline command pack when provisioning a new environment.
$ git clone <repo> __SLM__
$ cd __SLM__/backend && python -m venv .venv && .venv/bin/pip install -r requirements.txt
$ cd __SLM__/backend && .venv/bin/uvicorn app.main:app --reload --port 8000
$ cd __SLM__/backend && .venv/bin/celery -A app.worker.celery_app worker --loglevel=INFO --pool=threads --concurrency=2
$ cd __SLM__/frontend && npm install && npm run dev
$ ./brewslm project create --name demo-slm --template general
$ ./brewslm dataset import --project 1 --sample general-chat-v1
$ ./brewslm train --project 1 --autopilot --one-click
$ ./brewslm export --project 1 --format huggingface --target vllm