Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch
Paper • 2311.03099 • Published • 33
How to use Kame1024/evo-test-7b-01 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="Kame1024/evo-test-7b-01") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Kame1024/evo-test-7b-01")
model = AutoModelForCausalLM.from_pretrained("Kame1024/evo-test-7b-01")How to use Kame1024/evo-test-7b-01 with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Kame1024/evo-test-7b-01"
# 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-01",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/Kame1024/evo-test-7b-01
How to use Kame1024/evo-test-7b-01 with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "Kame1024/evo-test-7b-01" \
--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-01",
"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-01" \
--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-01",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use Kame1024/evo-test-7b-01 with Docker Model Runner:
docker model run hf.co/Kame1024/evo-test-7b-01
This is a merge of pre-trained language models created using mergekit.
This model was merged using the DARE TIES merge method using ./storage2/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: ./storage2/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, 8]
model: ./storage2/input_models/shisa-gamma-7b-v1_4025154171
parameters:
density: 0.6699910985974532
weight: 0.13529360500839205
- layer_range: [0, 8]
model: ./storage2/input_models/WizardMath-7B-V1.1_2027605156
parameters:
density: 0.8652557087160213
weight: 0.6985440552740758
- layer_range: [0, 8]
model: ./storage2/input_models/Abel-7B-002_121690448
parameters:
density: 0.4323464491414452
weight: 0.8179823325064868
- layer_range: [0, 8]
model: ./storage2/input_models/Mistral-7B-v0.1_8133861
- sources:
- layer_range: [8, 16]
model: ./storage2/input_models/shisa-gamma-7b-v1_4025154171
parameters:
density: 1.0
weight: 0.03216719764341956
- layer_range: [8, 16]
model: ./storage2/input_models/WizardMath-7B-V1.1_2027605156
parameters:
density: 0.6967615831667242
weight: 0.8043194027622319
- layer_range: [8, 16]
model: ./storage2/input_models/Abel-7B-002_121690448
parameters:
density: 0.7897142847167249
weight: 0.09233872355906134
- layer_range: [8, 16]
model: ./storage2/input_models/Mistral-7B-v0.1_8133861
- sources:
- layer_range: [16, 24]
model: ./storage2/input_models/shisa-gamma-7b-v1_4025154171
parameters:
density: 1.0
weight: 0.6740405166949244
- layer_range: [16, 24]
model: ./storage2/input_models/WizardMath-7B-V1.1_2027605156
parameters:
density: 0.5417954561416459
weight: 0.308476065247547
- layer_range: [16, 24]
model: ./storage2/input_models/Abel-7B-002_121690448
parameters:
density: 0.7841601014052402
weight: 0.02993327454595157
- layer_range: [16, 24]
model: ./storage2/input_models/Mistral-7B-v0.1_8133861
- sources:
- layer_range: [24, 32]
model: ./storage2/input_models/shisa-gamma-7b-v1_4025154171
parameters:
density: 0.5892764365325144
weight: 0.7288214753840682
- layer_range: [24, 32]
model: ./storage2/input_models/WizardMath-7B-V1.1_2027605156
parameters:
density: 0.8133101423312465
weight: 0.06233401147902682
- layer_range: [24, 32]
model: ./storage2/input_models/Abel-7B-002_121690448
parameters:
density: 0.9351019303077212
weight: 0.008694459163933368
- layer_range: [24, 32]
model: ./storage2/input_models/Mistral-7B-v0.1_8133861