Merge Models
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Models I merged using mergekit library • 8 items • Updated • 4
How to use suayptalha/HomerCreativeAnvita-Mix-Qw7B with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="suayptalha/HomerCreativeAnvita-Mix-Qw7B")
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe(messages) # Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM
tokenizer = AutoTokenizer.from_pretrained("suayptalha/HomerCreativeAnvita-Mix-Qw7B")
model = AutoModelForMultimodalLM.from_pretrained("suayptalha/HomerCreativeAnvita-Mix-Qw7B")
messages = [
{"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))How to use suayptalha/HomerCreativeAnvita-Mix-Qw7B with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "suayptalha/HomerCreativeAnvita-Mix-Qw7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "suayptalha/HomerCreativeAnvita-Mix-Qw7B",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'docker model run hf.co/suayptalha/HomerCreativeAnvita-Mix-Qw7B
How to use suayptalha/HomerCreativeAnvita-Mix-Qw7B with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "suayptalha/HomerCreativeAnvita-Mix-Qw7B" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "suayptalha/HomerCreativeAnvita-Mix-Qw7B",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'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 "suayptalha/HomerCreativeAnvita-Mix-Qw7B" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "suayptalha/HomerCreativeAnvita-Mix-Qw7B",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'How to use suayptalha/HomerCreativeAnvita-Mix-Qw7B with Docker Model Runner:
docker model run hf.co/suayptalha/HomerCreativeAnvita-Mix-Qw7B
This is a merge of pre-trained language models created using mergekit.
This model is currently ranked #3 on the Open LLM Leaderboard among models up to 8B parameters and #5 among models up to 13B parameters!
This model was merged using the SLERP merge method.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
base_model: ZeroXClem/Qwen2.5-7B-HomerCreative-Mix
dtype: bfloat16
merge_method: slerp
parameters:
t:
- filter: self_attn
value: [0.0, 0.5, 0.3, 0.7, 1.0]
- filter: mlp
value: [1.0, 0.5, 0.7, 0.3, 0.0]
- value: 0.5
slices:
- sources:
- layer_range: [0, 28]
model: ZeroXClem/Qwen2.5-7B-HomerCreative-Mix
- layer_range: [0, 28]
model: ZeroXClem/Qwen2.5-7B-HomerAnvita-NerdMix
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 34.62 |
| IFEval (0-Shot) | 78.08 |
| BBH (3-Shot) | 36.98 |
| MATH Lvl 5 (4-Shot) | 31.04 |
| GPQA (0-shot) | 8.61 |
| MuSR (0-shot) | 14.73 |
| MMLU-PRO (5-shot) | 38.28 |