Instructions to use Inv/Exponenta-Alpha-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Inv/Exponenta-Alpha-7B with Transformers:

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

pipe = pipeline("text-generation", model="Inv/Exponenta-Alpha-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Inv/Exponenta-Alpha-7B")
model = AutoModelForCausalLM.from_pretrained("Inv/Exponenta-Alpha-7B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Inv/Exponenta-Alpha-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Inv/Exponenta-Alpha-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Inv/Exponenta-Alpha-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Inv/Exponenta-Alpha-7B

SGLang

How to use Inv/Exponenta-Alpha-7B 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 "Inv/Exponenta-Alpha-7B" \
    --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": "Inv/Exponenta-Alpha-7B",
		"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 "Inv/Exponenta-Alpha-7B" \
        --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": "Inv/Exponenta-Alpha-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Inv/Exponenta-Alpha-7B with Docker Model Runner:
```
docker model run hf.co/Inv/Exponenta-Alpha-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Exponenta-Alpha-7B

This is a merge of pre-trained language models created using mergekit. I've used exponential function with base of 2 for this one.

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

Inv/Konstanta-V4-Alpha-7B
/content/drive/MyDrive/InfinityRP-Split

Configuration

The following YAML configuration was used to produce this model:


slices:
  - sources:
    - model: Inv/Konstanta-V4-Alpha-7B
      layer_range: [0,32]
    - model: /content/drive/MyDrive/InfinityRP-Split
      layer_range: [0,32]
merge_method: slerp
base_model: Inv/Konstanta-V4-Alpha-7B
parameters:
  t:
    - filter: self_attn
      value: [0.0002, 0.0004, 0.0008, 0.0016, 0.0032, 0.0064, 0.0128, 0.0256, 0.0512, 0.1024, 0.2048, 0.4096, 0.8192]
    - filter: mlp
      value: [0.8192, 0.4096, 0.2048, 0.1024, 0.0512, 0.0256, 0.0128, 0.0064, 0.0032, 0.0016, 0.0008, 0.0004, 0.0002]
    - value: 0.5
dtype: bfloat16