EnsembleLLM
Collection
2 items • Updated
How to use Akshaygande/EnsembleLLM with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="Akshaygande/EnsembleLLM") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Akshaygande/EnsembleLLM")
model = AutoModelForCausalLM.from_pretrained("Akshaygande/EnsembleLLM")How to use Akshaygande/EnsembleLLM with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Akshaygande/EnsembleLLM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Akshaygande/EnsembleLLM",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/Akshaygande/EnsembleLLM
How to use Akshaygande/EnsembleLLM with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "Akshaygande/EnsembleLLM" \
--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": "Akshaygande/EnsembleLLM",
"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 "Akshaygande/EnsembleLLM" \
--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": "Akshaygande/EnsembleLLM",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use Akshaygande/EnsembleLLM with Docker Model Runner:
docker model run hf.co/Akshaygande/EnsembleLLM
This is a merge of pre-trained language models created using mergekit.
This model was merged using the TIES merge method using C:\Users\LENOVO\Downloads\MMajor\mergekit\jaskier-7b-dpo-v5.6 as a base.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
models:
- model: C:\Users\LENOVO\Downloads\MMajor\mergekit\OGNO-7B
parameters:
density: 0.9
weight: 0.5
- model: C:\Users\LENOVO\Downloads\MMajor\mergekit\jaskier-7b-dpo-v5.6
parameters:
density: 0.5
weight: 0.3
merge_method: ties
base_model: C:\Users\LENOVO\Downloads\MMajor\mergekit\jaskier-7b-dpo-v5.6
parameters:
normalize: true
dtype: float16