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"])