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)

# pip install -U transformers accelerate
# 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 / alephlm.py

Commit History

Sep 23 26 days ago

19:38
load under transformers' remote-code loader: defer the imports it cannot resolve⁠

AbstractPhil

Verified signature
5e71e4f

Sep 19 30 days ago

20:28
mirror alephllm 0.9.0 (same-session law): presets.py (AlephLMConfig.fusion field, default None; the v3 preset builder + registrations guarded for copies without the data stack), governor.py (raw_block), alephlm.py (the optional fusion stage imported lazily — unfused crafts load without model/fusion.py; the trunk loop factored into _trunk). Gated: strict load of model.safetensors through the vendored path, logits parity 0.0 vs geolip.

AbstractPhil

Verified signature
fa82fde
17:48
mirror alephllm 0.8.9 (same-session law): presets.py (v3 preset builder + registrations guarded for copies without the data stack; TrainConfig.phase_lr_scale/phase_seed_offset; Preset.data_scale/epoch_cap/rebalance_to), governor.py (raw_block: nested wrappers), alephlm.py (compile_hubs via raw_block). Gated: strict load of model.safetensors through the vendored path, logits parity 0.0 vs geolip.

AbstractPhil

Verified signature
79d789d

Aug 26

18:02
mirror alephlm.py from geolip 0.7.0 @0c946b3 (hub_ckpt; default 0 = v1 verbatim)

AbstractPhil

Verified signature
64f2a91
17:45
mirror alephlm.py from geolip 0.7.0 @a63a29b (MIRROR law; n_const=1 bit-identical)

AbstractPhil

Verified signature
3dbc702

Aug 15

23:08
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

AbstractPhil

Verified signature
b007aec

Commit History

Sep 23 26 days ago

load under transformers' remote-code loader: defer the imports it cannot resolve⁠

Sep 19 30 days ago

Aug 26

mirror alephlm.py from geolip 0.7.0 @0c946b3 (hub_ckpt; default 0 = v1 verbatim)

mirror alephlm.py from geolip 0.7.0 @a63a29b (MIRROR law; n_const=1 bit-identical)

Aug 15

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