Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Paper • 2203.05482 • Published • 9
How to use Ignatfhc/mergekit-linear-ppluabk with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="Ignatfhc/mergekit-linear-ppluabk") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Ignatfhc/mergekit-linear-ppluabk")
model = AutoModelForCausalLM.from_pretrained("Ignatfhc/mergekit-linear-ppluabk", device_map="auto")How to use Ignatfhc/mergekit-linear-ppluabk with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ignatfhc/mergekit-linear-ppluabk"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Ignatfhc/mergekit-linear-ppluabk",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/Ignatfhc/mergekit-linear-ppluabk
How to use Ignatfhc/mergekit-linear-ppluabk with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "Ignatfhc/mergekit-linear-ppluabk" \
--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": "Ignatfhc/mergekit-linear-ppluabk",
"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 "Ignatfhc/mergekit-linear-ppluabk" \
--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": "Ignatfhc/mergekit-linear-ppluabk",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use Ignatfhc/mergekit-linear-ppluabk with Docker Model Runner:
docker model run hf.co/Ignatfhc/mergekit-linear-ppluabk
This is a merge of pre-trained language models created using mergekit.
This model was merged using the Linear merge method using huihui-ai/Huihui-gemma-3-270m-it-abliterated as a base.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
models:
- layer_range: [0, 12]
model: aifffffffd/Gemma-Thinking-Test
parameters:
weight: 1
density: 0.9
gamma: 0.01
normalize: true
int8_mask: true
random_seed: 0
temperature: 0.5
top_p: 0.65
inference: true
max_tokens: 999999999
stream: true
quantization:
- method: int8
value: 100
- method: int4
value: 100
- layer_range: [0, 12]
model: unsloth/gemma-3-270m-it
parameters:
weight: 1
density: 0.9
gamma: 0.01
normalize: true
int8_mask: true
random_seed: 0
temperature: 0.5
top_p: 0.65
inference: true
max_tokens: 999999999
stream: true
quantization:
- method: int8
value: 100
- method: int4
value: 100
- layer_range: [0, 12]
model: murat/kyrgyz_umlaut_corrector
parameters:
weight: 1
density: 0.9
gamma: 0.01
normalize: true
int8_mask: true
random_seed: 0
temperature: 0.5
top_p: 0.65
inference: true
max_tokens: 999999999
stream: true
quantization:
- method: int8
value: 100
- method: int4
value: 100
- layer_range: [0, 12]
model: unsloth/gemma-3-270m-it
parameters:
weight: 1
density: 0.9
gamma: 0.01
normalize: true
int8_mask: true
random_seed: 0
temperature: 0.5
top_p: 0.65
inference: true
max_tokens: 999999999
stream: true
quantization:
- method: int8
value: 100
- method: int4
value: 100
- layer_range: [0, 12]
model: tjefferson401/MyGemmaNPC
parameters:
weight: 1
density: 0.9
gamma: 0.01
normalize: true
int8_mask: true
random_seed: 0
temperature: 0.5
top_p: 0.65
inference: true
max_tokens: 999999999
stream: true
quantization:
- method: int8
value: 100
- method: int4
value: 100
- layer_range: [0, 12]
model: alakxender/gemma-3-270m-dhivehi-text-classifier
parameters:
weight: 1
density: 0.9
gamma: 0.01
normalize: true
int8_mask: true
random_seed: 0
temperature: 0.5
top_p: 0.65
inference: true
max_tokens: 999999999
stream: true
quantization:
- method: int8
value: 100
- method: int4
value: 100
- layer_range: [0, 12]
model: huihui-ai/Huihui-gemma-3-270m-it-abliterated
parameters:
weight: 1
density: 0.9
gamma: 0.01
normalize: true
int8_mask: true
random_seed: 0
temperature: 0.5
top_p: 0.65
inference: true
max_tokens: 999999999
stream: true
quantization:
- method: int8
value: 100
- method: int4
value: 100
- layer_range: [0, 12]
model: RohanSardar/mental-health-qa
parameters:
weight: 1
density: 0.9
gamma: 0.01
normalize: true
int8_mask: true
random_seed: 0
temperature: 0.5
top_p: 0.65
inference: true
max_tokens: 999999999
stream: true
quantization:
- method: int8
value: 100
- method: int4
value: 100
- layer_range: [0, 12]
model: xriminact/MyGemmaQuiz
parameters:
weight: 1
density: 0.9
gamma: 0.01
normalize: true
int8_mask: true
random_seed: 0
temperature: 0.5
top_p: 0.65
inference: true
max_tokens: 999999999
stream: true
quantization:
- method: int8
value: 100
- method: int4
value: 100
- layer_range: [0, 12]
model: NukeverseAi/HQQ-270M
parameters:
weight: 1
density: 0.9
gamma: 0.01
normalize: true
int8_mask: true
random_seed: 0
temperature: 0.5
top_p: 0.65
inference: true
max_tokens: 999999999
stream: true
quantization:
- method: int8
value: 100
- method: int4
value: 100
- layer_range: [0, 12]
model: clevrpwn/gemma-3-270m-codealpaca-finetune
parameters:
weight: 1
density: 0.9
gamma: 0.01
normalize: true
int8_mask: true
random_seed: 0
temperature: 0.5
top_p: 0.65
inference: true
max_tokens: 999999999
stream: true
quantization:
- method: int8
value: 100
- method: int4
value: 100
merge_method: linear
base_model: huihui-ai/Huihui-gemma-3-270m-it-abliterated
weight: 1
density: 0.9
gamma: 0.01
normalize: true
int8_mask: true
random_seed: 0
temperature: 0.5
top_p: 0.65
inference: true
max_tokens: 999999999
stream: true
quantization:
- method: int8
value: 100
- method: int4
value: 100
parameters:
weight: 1
density: 0.9
gamma: 0.01
normalize: true
int8_mask: true
random_seed: 0
temperature: 0.5
top_p: 0.65
inference: true
max_tokens: 999999999
stream: true
quantization:
- method: int8
value: 100
- method: int4
value: 100
dtype: float16