Instructions to use Kukedlc/Jupiter-k-7B-slerp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kukedlc/Jupiter-k-7B-slerp with Transformers:

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

pipe = pipeline("text-generation", model="Kukedlc/Jupiter-k-7B-slerp")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Kukedlc/Jupiter-k-7B-slerp")
model = AutoModelForCausalLM.from_pretrained("Kukedlc/Jupiter-k-7B-slerp")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Kukedlc/Jupiter-k-7B-slerp with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Kukedlc/Jupiter-k-7B-slerp"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kukedlc/Jupiter-k-7B-slerp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Kukedlc/Jupiter-k-7B-slerp

SGLang

How to use Kukedlc/Jupiter-k-7B-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 "Kukedlc/Jupiter-k-7B-slerp" \
    --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": "Kukedlc/Jupiter-k-7B-slerp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Kukedlc/Jupiter-k-7B-slerp" \
        --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": "Kukedlc/Jupiter-k-7B-slerp",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

Jupiter-k-7B-slerp ( My Favorite model! )

Jupiter-k-7B-slerp is a merge of the following models using LazyMergekit:

🧩 Configuration

models:
  - model: Kukedlc/NeuralContamination-7B-ties
    parameters:
      density: [1, 0.7, 0.1] # density gradient
      weight: 1.0
  - model: Kukedlc/NeuralTopBench-7B-ties
    parameters:
      density: 0.5
      weight: [0, 0.3, 0.7, 1] # weight gradient
  - model: Gille/StrangeMerges_32-7B-slerp
    parameters:
      density: 0.33
      weight:
        - filter: mlp
          value: 0.5
        - value: 0
merge_method: ties
base_model: Kukedlc/NeuralMaxime-7B-slerp 
parameters:
  normalize: true
  int8_mask: true
dtype: bfloat16

💻 Usage - Stream

# Requirements
!pip install -qU transformers accelerate bitsandbytes

# Imports & settings
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
import warnings
import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"
warnings.filterwarnings('ignore')

# Model & Tokenizer
MODEL_NAME = "Kukedlc/Jupiter-k-7B-slerp"
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, device_map='cuda:1', load_in_4bit=True)
tok = AutoTokenizer.from_pretrained(MODEL_NAME)

# Inference
prompt = "I want you to generate a theory that unites quantum mechanics with the theory of relativity and cosmic consciousness"
inputs = tok([prompt], return_tensors="pt").to('cuda')
streamer = TextStreamer(tok)

# Despite returning the usual output, the streamer will also print the generated text to stdout.
_ = model.generate(**inputs, streamer=streamer, max_new_tokens=512, do_sample=True, num_beams=1, top_p=0.9, temperature=0.7)

💻 Usage - Clasic

!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Kukedlc/Jupiter-k-7B-slerp"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)

messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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"])

Downloads last month: 69

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for Kukedlc/Jupiter-k-7B-slerp

Merges

5 models

Quantizations

2 models