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)

# pip install -U transformers accelerate
# 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 / config.json

bbkdevops

FABER-Genesis-A100-Max 13.86B architecture: config + HF modeling + profiles + benchmark harness

2ffbf1f verified 21 days ago

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{"architectures": ["FaberGenesisForCausalLM"], "auto_map": {"AutoConfig": "modeling_faber_genesis.FaberGenesisConfig", "AutoModelForCausalLM": "modeling_faber_genesis.FaberGenesisForCausalLM"}, "bf16_training_recipe": {"activation_checkpointing": true, "context": 512, "microbatch": 1, "optimizer": "AdamW8bit"}, "cass_state_dim": 1024, "ffn_dim": 16384, "hidden_size": 6144, "model_type": "faber-genesis", "n_kv_heads": 8, "n_recurrent": 1, "num_attention_heads": 48, "num_hidden_layers": 35, "num_logical_layers": 42, "num_physical_layers": 35, "param_total": 13860526082, "param_total_B": 13.860526082, "profile": "max-13.86B", "rms_norm_eps": 1e-05, "rope_theta": 10000.0, "share_map": [0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34], "tie_word_embeddings": true, "torch_dtype": "bfloat16", "transformers_version": "4.55.0", "use_cache": true, "vocab_size": 32768}