Instructions to use MatthieuJ/Jason1903_SLERP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MatthieuJ/Jason1903_SLERP with Transformers:

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

pipe = pipeline("text-generation", model="MatthieuJ/Jason1903_SLERP")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MatthieuJ/Jason1903_SLERP")
model = AutoModelForCausalLM.from_pretrained("MatthieuJ/Jason1903_SLERP")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use MatthieuJ/Jason1903_SLERP with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MatthieuJ/Jason1903_SLERP"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MatthieuJ/Jason1903_SLERP",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MatthieuJ/Jason1903_SLERP

SGLang

How to use MatthieuJ/Jason1903_SLERP 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 "MatthieuJ/Jason1903_SLERP" \
    --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": "MatthieuJ/Jason1903_SLERP",
		"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 "MatthieuJ/Jason1903_SLERP" \
        --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": "MatthieuJ/Jason1903_SLERP",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MatthieuJ/Jason1903_SLERP with Docker Model Runner:
```
docker model run hf.co/MatthieuJ/Jason1903_SLERP
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Jason1903_SLERP

Jason1903_SLERP is a merge of the following models using mergekit:

🧩 Configuration

slices:
  - sources:
      - model: yam-peleg/Experiment26-7B
        layer_range: [0, 32]
      - model: chihoonlee10/T3Q-Mistral-Orca-Math-DPO
        layer_range: [0, 32]
merge_method: slerp
base_model: chihoonlee10/T3Q-Mistral-Orca-Math-DPO
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: float16

# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |76.77|
|AI2 Reasoning Challenge (25-Shot)|73.12|
|HellaSwag (10-Shot)              |89.13|
|MMLU (5-Shot)                    |64.43|
|TruthfulQA (0-shot)              |78.13|
|Winogrande (5-shot)              |85.08|
|GSM8k (5-shot)                   |70.74|

Downloads last month: 58

Safetensors

Model size

7B params

Tensor type

F16

Model tree for MatthieuJ/Jason1903_SLERP

Merges

3 models

Quantizations

1 model