Instructions to use AbstractPhil/mini-beatrix-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AbstractPhil/mini-beatrix-1 with Transformers:

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

pipe = pipeline("text-generation", model="AbstractPhil/mini-beatrix-1", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("AbstractPhil/mini-beatrix-1", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AbstractPhil/mini-beatrix-1 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/AbstractPhil/mini-beatrix-1

SGLang

How to use AbstractPhil/mini-beatrix-1 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 "AbstractPhil/mini-beatrix-1" \
    --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": "AbstractPhil/mini-beatrix-1",
		"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 "AbstractPhil/mini-beatrix-1" \
        --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": "AbstractPhil/mini-beatrix-1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AbstractPhil/mini-beatrix-1 with Docker Model Runner:
```
docker model run hf.co/AbstractPhil/mini-beatrix-1
```

mini-beatrix-1 / README.md

AbstractPhil

mini-beatrix-1 pre-classroom annealment point: AutoModel-compatible (trust_remote_code), surgered 58,664 weights (head gate folded, semantic no-op), config from manifest, parity 0.00e+00 vs native stack, generate() verified

b007aec verified 2 days ago

preview code

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	---
	license: mit
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- byte-level
	- tokenizer-free
	- aleph
	- signed-address
	- custom_code
	---

	# mini-beatrix-1 — pretrain annealment point (pre-classroom)

	The locked pretrain+anneal state of mini-beatrix-1, a 112.5M-parameter
	byte-level AlephLM: step 58,664, 17.301B bytes seen
	(0.3B wikitext warmup · 15B fineweb-edu · 2B anneal mix), fineweb-holdout
	val 1.045 bits/byte. This checkpoint is the fixed departure point for
	the staged "early-life curriculum" — later classroom checkpoints live in
	the [training repo](https://huggingface.co/AbstractPhil/alephllm-mini-beatrix-training).

	No tokenizer: she reads raw UTF-8 bytes (`input_ids` = byte values
	0–255). Each position composes a byte trigram (dedicated pad row), so
	"tokens" are learned inside the network. Sixteen pre-norm layers where
	routing uses signed geometric addresses — `sinh/Σcosh` dispatch over
	learned unit anchors, inhibition as a first-class citizen, no softmax-
	over-choices, no top-k, no balance losses. Each layer carries an
	anchored FFN bank born contributing exactly zero; layers 4/9/14 use a
	linear-cost address read (CausalSplatHUB) instead of softmax attention.
	Both elected themselves into load-bearing work: at this checkpoint,
	removing the banks costs +2.25 bpb, removing the hub attention
	+3.73 bpb (toggle ledger, fineweb holdout). The dual head's aleph
	read is present with its gate folded to 1.0 (a verified semantic no-op,
	max\|logit diff\| 2.4e-07) and contributes 0.0000 bpb here — it is the
	live subject of the head-election experiment in the classroom phase.

	## Use

	```python
	import torch
	from transformers import AutoModelForCausalLM

	m = AutoModelForCausalLM.from_pretrained(
	"AbstractPhil/mini-beatrix-1", trust_remote_code=True).eval()

	ids = torch.tensor([list("The history of mathematics begins".encode())])
	out = m.generate(ids, max_new_tokens=96, do_sample=True,
	temperature=0.7, top_p=0.95)
	print(bytes(out[0].tolist()).decode("utf-8", errors="replace"))
	```

	Bits-per-byte on your own text: pass `labels=input_ids` (HF shift
	semantics are internal) and divide the returned loss (nats/byte) by
	`ln 2`. No KV cache in this wrapper — generation recomputes the prefix
	each step; for cached decode use the native stack below.

	## Honest notes

	- The 2B anneal mix included dialogue in her chat template and a small
	identity texture, so the bare model chats and knows her name —
	behavior we have since ruled OUT of core corpora (conditioning belongs
	in detachable arms; see the [amoe-lora](https://github.com/AbstractEyes/amoe-lora)
	arm system and `mini-beatrix-1/arms/` in the training repo).
	- Small and early: conversational in shape, thin on knowledge,
	confidently wrong at times. Curriculum probe baselines (P0–P8),
	toggle ledgers, and lexicon-census reports for this exact checkpoint
	are in the training repo under `mini-beatrix-1/reports/`.

	Code: [github.com/AbstractEyes/alephllm](https://github.com/AbstractEyes/alephllm) ·
	talk to her: [alephllm-chat](https://huggingface.co/spaces/AbstractPhil/alephllm-chat)