Quantization made by Richard Erkhov.

pandafish-7b - GGUF

Model creator: https://huggingface.co/ichigoberry/
Original model: https://huggingface.co/ichigoberry/pandafish-7b/

Name	Quant method	Size
pandafish-7b.Q2_K.gguf	Q2_K	2.53GB
pandafish-7b.IQ3_XS.gguf	IQ3_XS	2.81GB
pandafish-7b.IQ3_S.gguf	IQ3_S	2.96GB
pandafish-7b.Q3_K_S.gguf	Q3_K_S	2.95GB
pandafish-7b.IQ3_M.gguf	IQ3_M	3.06GB
pandafish-7b.Q3_K.gguf	Q3_K	3.28GB
pandafish-7b.Q3_K_M.gguf	Q3_K_M	3.28GB
pandafish-7b.Q3_K_L.gguf	Q3_K_L	3.56GB
pandafish-7b.IQ4_XS.gguf	IQ4_XS	3.67GB
pandafish-7b.Q4_0.gguf	Q4_0	3.83GB
pandafish-7b.IQ4_NL.gguf	IQ4_NL	3.87GB
pandafish-7b.Q4_K_S.gguf	Q4_K_S	3.86GB
pandafish-7b.Q4_K.gguf	Q4_K	4.07GB
pandafish-7b.Q4_K_M.gguf	Q4_K_M	4.07GB
pandafish-7b.Q4_1.gguf	Q4_1	4.24GB
pandafish-7b.Q5_0.gguf	Q5_0	4.65GB
pandafish-7b.Q5_K_S.gguf	Q5_K_S	4.65GB
pandafish-7b.Q5_K.gguf	Q5_K	4.78GB
pandafish-7b.Q5_K_M.gguf	Q5_K_M	4.78GB
pandafish-7b.Q5_1.gguf	Q5_1	5.07GB
pandafish-7b.Q6_K.gguf	Q6_K	5.53GB
pandafish-7b.Q8_0.gguf	Q8_0	7.17GB

Original model description:

tags: - merge - mergekit - lazymergekit license: apache-2.0

pandafish-7b

pandafish-7b is an instruct model based on a Model Stock merge of the following models (via LazyMergekit):

🧩 Configuration

models:
  - model: mistralai/Mistral-7B-v0.1
  - model: mistralai/Mistral-7B-Instruct-v0.2
  - model: CultriX/NeuralTrix-bf16
  - model: OpenPipe/mistral-ft-optimized-1227
merge_method: model_stock
base_model: mistralai/Mistral-7B-v0.1
dtype: bfloat16

🏆 Evals

Model	Average	AGIEval	GPT4All	TruthfulQA	Bigbench
pandafish-7b 📄	51.99	40	74.23	53.22	40.51
mistralai/Mistral-7B-Instruct-v0.2 📄	54.81	38.5	71.64	66.82	42.29

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "ichigoberry/pandafish-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"])