Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch
Paper • 2311.03099 • Published • 33
How to use JDBMG/Herdolphyr with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="JDBMG/Herdolphyr") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("JDBMG/Herdolphyr")
model = AutoModelForCausalLM.from_pretrained("JDBMG/Herdolphyr")How to use JDBMG/Herdolphyr with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JDBMG/Herdolphyr"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "JDBMG/Herdolphyr",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/JDBMG/Herdolphyr
How to use JDBMG/Herdolphyr with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "JDBMG/Herdolphyr" \
--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": "JDBMG/Herdolphyr",
"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 "JDBMG/Herdolphyr" \
--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": "JDBMG/Herdolphyr",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use JDBMG/Herdolphyr with Docker Model Runner:
docker model run hf.co/JDBMG/Herdolphyr
This is a merge of pre-trained language models created using mergekit.
This model was merged using the DARE TIES merge method using mistralai/Mistral-7B-v0.1 as a base.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
models:
- model: cognitivecomputations/dolphin-2.2.1-mistral-7b
parameters:
density: [1, 0.7, 0.1] # density gradient
weight: 1.0
- model: HuggingFaceH4/zephyr-7b-beta
parameters:
density: 0.5
weight: [0, 0.3, 0.7, 1] # weight gradient
- model: NousResearch/Hermes-2-Pro-Mistral-7B
parameters:
density: 0.33
weight:
- filter: mlp
value: 0.5
- value: 0
merge_method: dare_ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
normalize: true
int8_mask: true
dtype: float16