Instructions to use GerbilLab/GerbilBlender-A-32m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GerbilLab/GerbilBlender-A-32m with Transformers:

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

pipe = pipeline("text-generation", model="GerbilLab/GerbilBlender-A-32m")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("GerbilLab/GerbilBlender-A-32m")
model = AutoModelForCausalLM.from_pretrained("GerbilLab/GerbilBlender-A-32m", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use GerbilLab/GerbilBlender-A-32m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GerbilLab/GerbilBlender-A-32m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GerbilLab/GerbilBlender-A-32m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/GerbilLab/GerbilBlender-A-32m

SGLang

How to use GerbilLab/GerbilBlender-A-32m 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 "GerbilLab/GerbilBlender-A-32m" \
    --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": "GerbilLab/GerbilBlender-A-32m",
		"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 "GerbilLab/GerbilBlender-A-32m" \
        --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": "GerbilLab/GerbilBlender-A-32m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use GerbilLab/GerbilBlender-A-32m with Docker Model Runner:
```
docker model run hf.co/GerbilLab/GerbilBlender-A-32m
```

Model Name	Parameters	Class	Ratio	Tokens	Batch Size (Tokens)	Training Loss ↓
GerbilLab/GerbilBlender-A-32m	32m	A-Class	20	640M	262K	4.127

"Blender" models, inspired by UL2 pretraining, are trained equally in fill-in-the-middle, causal modelling, and masked language modelling tasks. Special tokens for these models include:

'<fitm_start>', '<multiple_tok_mask>', '<fitm_result>', '<causal>', '<mlm_start>', '<single_tok_mask>', '<mlm_end>'

# Example fill in the middle
'<fitm_start> this is an <multiple_tok_mask> for fill-in-the-middle <fitm_result> example text <|endoftext|>'

# Example causal language modelling
'<causal> this is an example text for causal language modelling <|endoftext|>'

# Example masked language modelling
'<mlm_start> this is an <single_tok_mask> text for masked language modelling <mlm_end> example <|endoftext|>'

Downloads last month: 15

Safetensors

Model size

65.6M params

Tensor type

F32

BOOL