Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch
Paper • 2311.03099 • Published • 36
How to use Kame1024/evo-test-7b-02 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="Kame1024/evo-test-7b-02") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Kame1024/evo-test-7b-02")
model = AutoModelForCausalLM.from_pretrained("Kame1024/evo-test-7b-02", device_map="auto")How to use Kame1024/evo-test-7b-02 with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Kame1024/evo-test-7b-02"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Kame1024/evo-test-7b-02",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/Kame1024/evo-test-7b-02
How to use Kame1024/evo-test-7b-02 with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "Kame1024/evo-test-7b-02" \
--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": "Kame1024/evo-test-7b-02",
"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 "Kame1024/evo-test-7b-02" \
--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": "Kame1024/evo-test-7b-02",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use Kame1024/evo-test-7b-02 with Docker Model Runner:
docker model run hf.co/Kame1024/evo-test-7b-02
This is a merge of pre-trained language models created using mergekit.
This model was merged using the DARE TIES merge method using ./storage3/input_models/Mistral-7B-v0.1_8133861 as a base.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
base_model: ./storage3/input_models/Mistral-7B-v0.1_8133861
dtype: bfloat16
merge_method: dare_ties
parameters:
int8_mask: 1.0
normalize: 1.0
slices:
- sources:
- layer_range: [0, 32]
model: ./storage3/input_models/shisa-gamma-7b-v1_4025154171
parameters:
density: 1.0
weight: -0.0378726672672588
- layer_range: [0, 32]
model: ./storage3/input_models/WizardMath-7B-V1.1_2027605156
parameters:
density: 0.7433311818361178
weight: 1.5192904356611323
- layer_range: [0, 32]
model: ./storage3/input_models/Abel-7B-002_121690448
parameters:
density: 0.47833652897680473
weight: 1.0403117323704718
- layer_range: [0, 32]
model: ./storage3/input_models/Mistral-7B-v0.1_8133861