Instructions to use bbkdevops/FABER-Genesis-A100-Max with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bbkdevops/FABER-Genesis-A100-Max with Transformers:

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

pipe = pipeline("text-generation", model="bbkdevops/FABER-Genesis-A100-Max", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("bbkdevops/FABER-Genesis-A100-Max", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use bbkdevops/FABER-Genesis-A100-Max with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bbkdevops/FABER-Genesis-A100-Max"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bbkdevops/FABER-Genesis-A100-Max",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bbkdevops/FABER-Genesis-A100-Max

SGLang

How to use bbkdevops/FABER-Genesis-A100-Max 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 "bbkdevops/FABER-Genesis-A100-Max" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bbkdevops/FABER-Genesis-A100-Max",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "bbkdevops/FABER-Genesis-A100-Max" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bbkdevops/FABER-Genesis-A100-Max",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use bbkdevops/FABER-Genesis-A100-Max with Docker Model Runner:
```
docker model run hf.co/bbkdevops/FABER-Genesis-A100-Max
```

FABER-Genesis-A100-Max (13.86B architecture)

Decoder-only transformer with weight sharing + CASS state/value/halt heads.

hidden 6144, heads 48, KV 8, head-dim 128, SwiGLU FFN 16384
vocab 32768, tied embeddings, RMSNorm, RoPE, GQA
35 physical blocks -> 42 logical transformations + 1 recurrent pass
total params: 13.860526082B (backbone + CASS)
training recipe: BF16, AdamW8bit, activation checkpointing, microbatch=1, ctx=512

Profiles

profile	physical	params	status
production-13.08B	33	13.0804B	default for long runs (~73.55 GiB)
safe-13.47B	34	13.4704B	squeeze with safety (~75.75 GiB)
max-13.86B	35	13.8605B	capacity experiments (~77.94 GiB)
oom-14.25B	36	14.2506B	OOM on single A100 AdamW8bit

What this repo contains

config.json (max profile), modeling_faber_genesis.py (self-contained HF model code), genesis_profiles.json, tokenizer_config.json (placeholder config, vocab 32768 — train a real tokenizer before pretraining), benchmark_genesis_a100.py, EVIDENCE_GENESIS_A100_MAX.md.

No trained weights are included in this upload. config.json defines a random-init architecture. To materialize weights:

from modeling_faber_genesis import FaberGenesisConfig, FaberGenesisForCausalLM
cfg = FaberGenesisConfig()  # max-13.86B
model = FaberGenesisForCausalLM(cfg)  # random init, BF16 training recipe applies on A100

Ceiling benchmark (run on A100 80GB)

pip install "torch==2.6.0" bitsandbytes transformers
python benchmark_genesis_a100.py --profile max-13.86B --steps 1
python benchmark_genesis_a100.py --sweep --n-list 25,29,32,33,34,35,36,37 --steps 1

Pass = fwd+bwd+optim step completes; n=36/37 must OOM. VRAM estimates in EVIDENCE_GENESIS_A100_MAX.md are analytic, not a substitute for this run.

Limitations

Random-init step-pass says nothing about Thai/code/reasoning quality. Quality requires pretraining curriculum + CASS + verifier + agent training and SWE/APEX evals. 14.25B+ is not a single-A100 resident AdamW8bit config.

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