Instructions to use HomeBrewedLabs/metallum-1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HomeBrewedLabs/metallum-1b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="HomeBrewedLabs/metallum-1b", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("HomeBrewedLabs/metallum-1b", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use HomeBrewedLabs/metallum-1b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HomeBrewedLabs/metallum-1b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HomeBrewedLabs/metallum-1b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/HomeBrewedLabs/metallum-1b

SGLang

How to use HomeBrewedLabs/metallum-1b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "HomeBrewedLabs/metallum-1b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HomeBrewedLabs/metallum-1b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "HomeBrewedLabs/metallum-1b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HomeBrewedLabs/metallum-1b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use HomeBrewedLabs/metallum-1b with Docker Model Runner:
```
docker model run hf.co/HomeBrewedLabs/metallum-1b
```

Metallum-1B

A 1.0B-parameter ML/LLM-engineering specialist, pretrained from random initialization on a single RTX 5090 — 16.0B tokens in ~13.5 days including gates and recoveries — then one bounded knowledge/format SFT stage. Not a general chatbot. By Home Brewed Labs (project codename MetaLLM).

The engineering core of the project is measurement discipline: every training stage was gated by frozen batteries with exact checkpoint identity, replay accounting (≈1.04B replayed tokens from two hardware incidents, fully receipted), independent adversarial review of evaluation machinery, and a sealed never-trained-on final holdout (H9) reserved for a single one-time qualification.

Architecture

26-layer decoder, d_model 1792, GQA 28Q/14KV, SwiGLU (d_ff 4864), RMSNorm pre-norm + QK-norm + z-loss, RoPE θ=500k with NoPE every 4th layer (length-generalizing retrieval heads), document-masked attention, 40k byte-level BPE with FIM sentinels, 2048 native context. Muon (2D) + AdamW, WSD schedule, curriculum with short-sequence early phases.

Loading

from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("HomeBrewedlabs/metallum-1b",
                                             trust_remote_code=True)
tok = AutoTokenizer.from_pretrained("HomeBrewedlabs/metallum-1b")

trust_remote_code=True is required: this is a custom architecture (QK-norm, NoPE interleave, document masking). Do not map to a stock Llama config — that silently drops QK-norm/NoPE and produces a different model than the one measured below. Stock GGUF/vLLM converters reject this architecture by design; a faithful llama.cpp port is on the roadmap.

Evaluation (champion s2b075, frozen batteries, repeated where noted)

Capability	Metric	Metallum-1B	Qwen3-1.7B	Qwen3-0.6B	SmolLM2-1.7B
In-domain ML-eng problems	ineval_v1, frozen 150-task exec suite	7/150	7/150	3/150	0/150
In-domain code (internal suite)	code_v2 40-task, 2 exact repeats	32/40 (disclosure: known training overlap)	—	—	—
Long-context retrieval	RULER (90) / needles D1024+D2032 (18)	86/90 and 18/18	—	—	—
Base LM efficiency	held-out ML-arXiv BPC	0.7276	—	—	—
Final unseen generalization	H9 sealed holdout BPC (one-time run)	1.3953	—	—	—

All numbers were produced by frozen evaluators with sealed checkpoint identity; the ineval suite was used for model selection during development (selection-aware caveat), which is why the untouched H9 result is the number that matters for release claims.

Structured output: by decoding, not by hope

Base weights score 0/60 on a strict whole-output format suite (JSON schema / tool-call / MCQ-letter), and three bounded SFT recipes plus a ReST-EM screen all failed to move that (all receipted). This model therefore ships with decode-time structure enforcement: serve_metallum.py (included) constrains generation with a token-level JSON grammar + schema-forced keys, and scores 60/60 on the same frozen suite. If you consume the raw weights without the wrapper, expect prose, not JSON.

python serve_metallum.py --port 8399   # localhost endpoint: /generate, modes free|json|tool|mcq

Intended use

ML/LLM-engineering assistant: PyTorch/training scaffolding, concept explanation, long-context retrieval over technical docs, structured-output endpoints via the wrapper. Out of scope: general chat, non-ML factual QA, general-purpose coding, safety-critical use, autonomous code execution.

Honest limitations

neural_ops floor exception: this checkpoint scores 4/5 on the internal neural-ops category vs the 5/5 preregistered floor. The exception was adjudicated explicitly by the owner; it is disclosed, not hidden.
MCF (250q multiple-choice) is permanently non-promotable: the teacher generation prompts embedded real MCF items, so no MCF number from this lineage is valid evidence. Do not report one.
ineval is selection-aware: 7/150 was measured on a suite used during development. The sealed H9 result (1.3953 BPC on 400k post-cutoff Wikipedia characters, single run, contamination-verified at 0.0099% shingle containment) is the generalization claim.
Free generation makes local factual slips; verify specifics.
No preference/safety alignment; no RLHF.
Format behavior requires the constrained decoder (above).

Training data

Pretraining: 19.399B effective tokens (8.473B unique): 53.6% code, 19.6% knowledge, 15.2% reasoning, 11.6% math. 1.24% (240.6M tokens) is provider-teacher-derived; full lineage map is maintained by the authors. SFT (12,496 rows): 40% locally self-hosted open-teacher MCQs (qwen2.5-coder:14b, Apache-2.0), 12% self-generated synthetic retrieval rows, 48% provider-teacher-derived replay (disposition: owner written-rights determination, on file and available on request).

Attribution — Stack Exchange (stack_ml_edu, 2.973B pack tokens, 15.3% of the pack): CC BY-SA 4.0. Attribution is owed to Stack Exchange contributors, and the share-alike obligation attaches to that portion of the training data.

License

Apache-2.0 (model weights + code). See the attribution note above regarding training-data share-alike obligations.

Citation

Technical report in preparation.

Downloads last month: 9

Safetensors

Model size

1B params

Tensor type

F32

BF16