tags, base_model, license

tags	base_model	license
merge mergekit lazymergekit CultriX/MergeCeption-7B-v3	CultriX/MergeCeption-7B-v3	apache-2.0

pandafish-dt-7b

pandafish-dt-7b is a dare_ties merge of Experiment26-7B and MergeCeption-7B-v3 using LazyMergekit by mlabonne

💬 Try it

Playground on Huggingface Space

⚡ Quantized models

• GGUF: ichigoberry/pandafish-dt-7b-GGUF
• GGUF (w/ IQ): mradermacher/pandafish-dt-7b-GGUF
• MLX: 4bit 8bit

🏆 Evals

Evals from the Nous Benchmark suite:

Model	Average	AGIEval	GPT4All	TruthfulQA	Bigbench
AlphaMonarch-7B 📄	62.74	45.37	77.01	78.39	50.2
Monarch-7B 📄	62.68	45.48	77.07	78.04	50.14
🐡 pandafish-dt-7b 📄	62.65	45.24	77.19	78.41	49.76
MonarchPipe-7B-slerp 📄	58.77	46.12	74.89	66.59	47.49
NeuralHermes-2.5-Mistral-7B 📄	53.51	43.67	73.24	55.37	41.76
Mistral-7B-Instruct-v0.2 📄	54.81	38.5	71.64	66.82	42.29
OpenHermes-2.5-Mistral-7B 📄	52.42	42.75	72.99	52.99	40.94
pandafish-7b 📄	51.99	40	74.23	53.22	40.51

🧩 Configuration

models:
  - model: yam-peleg/Experiment26-7B
    # No parameters necessary for base model
  - model: CultriX/MergeCeption-7B-v3
    parameters:
      density: 0.53
      weight: 0.4
merge_method: dare_ties
base_model: yam-peleg/Experiment26-7B
parameters:
  int8_mask: true
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "ichigoberry/pandafish-dt-7b"
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"])