Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch
Paper • 2311.03099 • Published • 36
How to use ssktora/ja_e57b_merge_tydija with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="ssktora/ja_e57b_merge_tydija") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("ssktora/ja_e57b_merge_tydija")
model = AutoModelForCausalLM.from_pretrained("ssktora/ja_e57b_merge_tydija", device_map="auto")How to use ssktora/ja_e57b_merge_tydija with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ssktora/ja_e57b_merge_tydija"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "ssktora/ja_e57b_merge_tydija",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/ssktora/ja_e57b_merge_tydija
How to use ssktora/ja_e57b_merge_tydija with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "ssktora/ja_e57b_merge_tydija" \
--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": "ssktora/ja_e57b_merge_tydija",
"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 "ssktora/ja_e57b_merge_tydija" \
--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": "ssktora/ja_e57b_merge_tydija",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use ssktora/ja_e57b_merge_tydija with Docker Model Runner:
docker model run hf.co/ssktora/ja_e57b_merge_tydija
This is a merge of pre-trained language models created using mergekit.
This model was merged using the DARE TIES merge method using ./evol_merge_storage_ja_e57b_tydija/input_models/e5-mistral-7b-instruct_2385958088 as a base.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
base_model: ./evol_merge_storage_ja_e57b_tydija/input_models/e5-mistral-7b-instruct_2385958088
dtype: bfloat16
merge_method: dare_ties
parameters:
int8_mask: 1.0
normalize: 1.0
slices:
- sources:
- layer_range: [0, 4]
model: ./evol_merge_storage_ja_e57b_tydija/input_models/e5-mistral-7b-instruct_2385958088
parameters:
density: 0.9567125290278002
weight: 0.7329399429419414
- layer_range: [0, 4]
model: ./evol_merge_storage_ja_e57b_tydija/input_models/japanese-stablelm-base-gamma-7b_545310900
parameters:
density: 1.0
weight: 0.6175016127199866
- sources:
- layer_range: [4, 8]
model: ./evol_merge_storage_ja_e57b_tydija/input_models/e5-mistral-7b-instruct_2385958088
parameters:
density: 0.7351530312710608
weight: 0.9361918263111237
- layer_range: [4, 8]
model: ./evol_merge_storage_ja_e57b_tydija/input_models/japanese-stablelm-base-gamma-7b_545310900
parameters:
density: 1.0
weight: 0.6261500333536962
- sources:
- layer_range: [8, 12]
model: ./evol_merge_storage_ja_e57b_tydija/input_models/e5-mistral-7b-instruct_2385958088
parameters:
density: 1.0
weight: 0.38219531855733224
- layer_range: [8, 12]
model: ./evol_merge_storage_ja_e57b_tydija/input_models/japanese-stablelm-base-gamma-7b_545310900
parameters:
density: 0.9560023967707558
weight: 0.4847363738604221
- sources:
- layer_range: [12, 16]
model: ./evol_merge_storage_ja_e57b_tydija/input_models/e5-mistral-7b-instruct_2385958088
parameters:
density: 1.0
weight: 0.9760238855152437
- layer_range: [12, 16]
model: ./evol_merge_storage_ja_e57b_tydija/input_models/japanese-stablelm-base-gamma-7b_545310900
parameters:
density: 0.9962392996911643
weight: 0.6535045223316338
- sources:
- layer_range: [16, 20]
model: ./evol_merge_storage_ja_e57b_tydija/input_models/e5-mistral-7b-instruct_2385958088
parameters:
density: 1.0
weight: 0.32930274558082606
- layer_range: [16, 20]
model: ./evol_merge_storage_ja_e57b_tydija/input_models/japanese-stablelm-base-gamma-7b_545310900
parameters:
density: 0.9095162947498548
weight: 0.7439598517576353
- sources:
- layer_range: [20, 24]
model: ./evol_merge_storage_ja_e57b_tydija/input_models/e5-mistral-7b-instruct_2385958088
parameters:
density: 0.756559714204041
weight: 0.44719009636986334
- layer_range: [20, 24]
model: ./evol_merge_storage_ja_e57b_tydija/input_models/japanese-stablelm-base-gamma-7b_545310900
parameters:
density: 1.0
weight: 0.348213220068222
- sources:
- layer_range: [24, 28]
model: ./evol_merge_storage_ja_e57b_tydija/input_models/e5-mistral-7b-instruct_2385958088
parameters:
density: 1.0
weight: 0.5663144522369852
- layer_range: [24, 28]
model: ./evol_merge_storage_ja_e57b_tydija/input_models/japanese-stablelm-base-gamma-7b_545310900
parameters:
density: 1.0
weight: 0.5221351804388025
- sources:
- layer_range: [28, 32]
model: ./evol_merge_storage_ja_e57b_tydija/input_models/e5-mistral-7b-instruct_2385958088
parameters:
density: 1.0
weight: 0.3974171827818004
- layer_range: [28, 32]
model: ./evol_merge_storage_ja_e57b_tydija/input_models/japanese-stablelm-base-gamma-7b_545310900
parameters:
density: 0.9839795858964644
weight: 0.2576111697863762