Instructions to use Gille/StrangeMerges_52-7B-dare_ties with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Gille/StrangeMerges_52-7B-dare_ties with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Gille/StrangeMerges_52-7B-dare_ties")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Gille/StrangeMerges_52-7B-dare_ties")
model = AutoModelForCausalLM.from_pretrained("Gille/StrangeMerges_52-7B-dare_ties")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Gille/StrangeMerges_52-7B-dare_ties with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Gille/StrangeMerges_52-7B-dare_ties"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Gille/StrangeMerges_52-7B-dare_ties",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Gille/StrangeMerges_52-7B-dare_ties

SGLang

How to use Gille/StrangeMerges_52-7B-dare_ties with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Gille/StrangeMerges_52-7B-dare_ties" \
    --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": "Gille/StrangeMerges_52-7B-dare_ties",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

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 "Gille/StrangeMerges_52-7B-dare_ties" \
        --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": "Gille/StrangeMerges_52-7B-dare_ties",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Gille/StrangeMerges_52-7B-dare_ties with Docker Model Runner:
```
docker model run hf.co/Gille/StrangeMerges_52-7B-dare_ties
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

StrangeMerges_52-7B-dare_ties

StrangeMerges_52-7B-dare_ties is a merge of the following models using LazyMergekit:

🧩 Configuration

models:
  - model: Gille/StrangeMerges_51-7B-dare_ties
    # No parameters necessary for base model
  - model: WizardLM/WizardMath-7B-V1.1
    parameters:
      density: 0.66
      weight: 0.2
  - model: AurelPx/Percival_01-7b-slerp
    parameters:
      density: 0.55
      weight: 0.2
  - model: Weyaxi/Einstein-v4-7B
    parameters:
      density: 0.55
      weight: 0.2
  - model: Kukedlc/NeuralMaths-Experiment-7b
    parameters:
      density: 0.44
      weight: 0.2
  - model: Gille/StrangeMerges_35-7B-slerp
    parameters:
      density: 0.66
      weight: 0.2
merge_method: dare_ties
base_model: Gille/StrangeMerges_51-7B-dare_ties
parameters:
  int8_mask: true
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Gille/StrangeMerges_52-7B-dare_ties"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	73.51
AI2 Reasoning Challenge (25-Shot)	69.03
HellaSwag (10-Shot)	87.15
MMLU (5-Shot)	64.94
TruthfulQA (0-shot)	65.76
Winogrande (5-shot)	81.93
GSM8k (5-shot)	72.25

Downloads last month: 89

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for Gille/StrangeMerges_52-7B-dare_ties

AurelPx/Percival_01-7b-slerp

Gille/StrangeMerges_35-7B-slerp

Kukedlc/NeuralMaths-Experiment-7b

Weyaxi/Einstein-v4-7B

WizardLMTeam/WizardMath-7B-V1.1

Merge model

this model

Quantizations

2 models

Spaces using Gille/StrangeMerges_52-7B-dare_ties 8

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

69.030
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

87.150
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

64.940
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

65.760
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

81.930
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

72.250