Funny functions SLERP merges
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Regular gradient SLERP are boring. Let's try smth more FUN! • 2 items • Updated
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")How to use Inv/Gamma-Alpha-7B with vLLM:
# 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
}'docker model run hf.co/Inv/Gamma-Alpha-7B
How to use Inv/Gamma-Alpha-7B with SGLang:
# 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
}'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
}'How to use Inv/Gamma-Alpha-7B with Docker Model Runner:
docker model run hf.co/Inv/Gamma-Alpha-7B
This is a merge of pre-trained language models created using mergekit. I've used gamma function for this one with x in range (1,10)
This model was merged using the SLERP merge method.
The following models were included in the merge:
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: [0.107, 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