Instructions to use gszauer/Gab1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gszauer/Gab1B with Transformers:

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

pipe = pipeline("text-generation", model="gszauer/Gab1B", 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("gszauer/Gab1B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use gszauer/Gab1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gszauer/Gab1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gszauer/Gab1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/gszauer/Gab1B

SGLang

How to use gszauer/Gab1B 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 "gszauer/Gab1B" \
    --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": "gszauer/Gab1B",
		"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 "gszauer/Gab1B" \
        --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": "gszauer/Gab1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use gszauer/Gab1B with Docker Model Runner:
```
docker model run hf.co/gszauer/Gab1B
```

Gab1B / tokenizer_config.json

gszauer

Gab 1B: weights, Transformers port, model card

c3e462e verified 1 day ago

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348 Bytes

{
  "tokenizer_class": "GiftOfGabTokenizer",
  "auto_map": {
    "AutoTokenizer": ["tokenization_gift_of_gab.GiftOfGabTokenizer", null]
  },
  "model_max_length": 2048,
  "eos_token": "<|end|>",
  "pad_token": "<|pad|>",
  "additional_special_tokens": [
    "<|user|>",
    "<|assistant|>",
    "<|endoftext|>",
    "<think>",
    "</think>"
  ]
}

	{
	"tokenizer_class": "GiftOfGabTokenizer",
	"auto_map": {
	"AutoTokenizer": ["tokenization_gift_of_gab.GiftOfGabTokenizer", null]
	},
	"model_max_length": 2048,
	"eos_token": "<\|end\|>",
	"pad_token": "<\|pad\|>",
	"additional_special_tokens": [
	"<\|user\|>",
	"<\|assistant\|>",
	"<\|endoftext\|>",
	"<think>",
	"</think>"
	]
	}