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 / tokenizer_config.json

bbkdevops

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

2ffbf1f verified 18 days ago

Raw History Blame Contribute Delete

316 Bytes

{"add_bos_token": true, "add_eos_token": true, "bos_token": "<s>", "chat_template": "{% for m in messages %}{{ m.role + ': ' + m.content + '\n' }}{% endfor %}", "eos_token": "</s>", "model_max_length": 512, "pad_token": "</s>", "tokenizer_class": "PreTrainedTokenizerFast", "unk_token": "<unk>", "vocab_size": 32768}