Instructions to use jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES with Transformers:

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

pipe = pipeline("text-generation", model="jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES")
model = AutoModelForCausalLM.from_pretrained("jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES

SGLang

How to use jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES 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 "jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES" \
    --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": "jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES",
		"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 "jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES" \
        --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": "jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES with Docker Model Runner:
```
docker model run hf.co/jsfs11/RandomMergeNoNormWEIGHTED-7B-DARETIES
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

RandomMergeNoNormWEIGHTED-7B-DARETIES

RandomMergeNoNormWEIGHTED-7B-DARETIES is a merge of the following models using mergekit:

🧩 Configuration

models:
  - model: FelixChao/WestSeverus-7B-DPO-v2
    # No parameters necessary for base model
  - model: FelixChao/WestSeverus-7B-DPO-v2
    parameters:
      density: [1, 0.7, 0.1]
      weight: [0, 0.3, 0.7, 1]
  - model: CultriX/Wernicke-7B-v9
    parameters:
      density: [1, 0.7, 0.3]
      weight: [0, 0.25, 0.5, 1]
  - model: mlabonne/NeuralBeagle14-7B
    parameters:
      density: 0.25
      weight:
        - filter: mlp
          value: 0.5
        - value: 0
merge_method: ties
base_model: FelixChao/WestSeverus-7B-DPO-v2
parameters:
  int8_mask: true
  normalize: true
  sparsify:
    - filter: mlp
      value: 0.5
    - filter: self_attn
      value: 0.5
dtype: float16

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	75.36
AI2 Reasoning Challenge (25-Shot)	73.38
HellaSwag (10-Shot)	88.50
MMLU (5-Shot)	64.94
TruthfulQA (0-shot)	71.50
Winogrande (5-shot)	83.58
GSM8k (5-shot)	70.28