Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch
Paper • 2311.03099 • Published • 33
How to use Masterjp123/P1 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="Masterjp123/P1") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Masterjp123/P1")
model = AutoModelForCausalLM.from_pretrained("Masterjp123/P1")How to use Masterjp123/P1 with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Masterjp123/P1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Masterjp123/P1",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/Masterjp123/P1
How to use Masterjp123/P1 with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "Masterjp123/P1" \
--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": "Masterjp123/P1",
"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 "Masterjp123/P1" \
--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": "Masterjp123/P1",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use Masterjp123/P1 with Docker Model Runner:
docker model run hf.co/Masterjp123/P1
This is a merge of pre-trained language models created using mergekit.
This model was merged using the DARE TIES merge method using NousResearch/Meta-Llama-3-8B as a base.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
base_model: NousResearch/Meta-Llama-3-8B
dtype: bfloat16
merge_method: dare_ties
parameters:
int8_mask: 1.0
slices:
- sources:
- layer_range: [0, 32]
model: Weyaxi/Einstein-v6.1-Llama3-8B
parameters:
density: 0.1
weight: 1.0
- layer_range: [0, 32]
model: asiansoul/Versatile-Llama-3-8B-1m
parameters:
density: 0.2
weight: 0.35
- layer_range: [0, 32]
model: NousResearch/Hermes-2-Pro-Llama-3-8B
parameters:
density: 0.5
weight: 0.23
- layer_range: [0, 32]
model: NousResearch/Meta-Llama-3-8B