Instructions to use Inv/Gamma-Alpha-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Inv/Gamma-Alpha-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Inv/Gamma-Alpha-7B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Inv/Gamma-Alpha-7B") model = AutoModelForCausalLM.from_pretrained("Inv/Gamma-Alpha-7B") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Inv/Gamma-Alpha-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Inv/Gamma-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/Gamma-Alpha-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Inv/Gamma-Alpha-7B
- SGLang
How to use Inv/Gamma-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/Gamma-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/Gamma-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/Gamma-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/Gamma-Alpha-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Inv/Gamma-Alpha-7B with Docker Model Runner:
docker model run hf.co/Inv/Gamma-Alpha-7B
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base_model:
- Inv/Konstanta-V4-Alpha-7B
library_name: transformers
tags:
- mergekit
- merge
license: apache-2.0
---
# Gamma-Alpha-7B
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). I've used gamma function for this one with x in range (1,10)
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* /content/drive/MyDrive/InfinityRP-Split
* [Inv/Konstanta-V4-Alpha-7B](https://huggingface.co/Inv/Konstanta-V4-Alpha-7B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
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: [0.107, 0.116, 0.13, 0.149, 0.177, 0.222, 0.299, 0.459, 0.951]
- filter: mlp
value: [0.951, 0.459, 0.299, 0.222, 0.177, 0.149, 0.13, 0.116, 0.107]
- value: 0.5
dtype: bfloat16
``` |