CoolFace
Modelpublic

RichardErkhov/ichigoberry_-_pandafish-2-7b-32k-gguf

sourceHugging Faceupdated 2y agoView on Hugging Face
0likes1.1kdownloads
Model Card

Quantization made by Richard Erkhov.

Github

Discord

Request more models

pandafish-2-7b-32k - GGUF

  • —Model creator: https://huggingface.co/ichigoberry/
  • —Original model: https://huggingface.co/ichigoberry/pandafish-2-7b-32k/

Original model description: --- tags:

  • —merge
  • —mergekit
  • —lazymergekit
  • —mistralai/Mistral-7B-Instruct-v0.2
  • —cognitivecomputations/dolphin-2.8-mistral-7b-v02 base_model:
  • —mistralai/Mistral-7B-Instruct-v0.2
  • —cognitivecomputations/dolphin-2.8-mistral-7b-v02 license: apache-2.0 ---

<img src="https://cdn-uploads.huggingface.co/production/uploads/6389d3c61e8755d777902366/-_AiKUEsY3x-N7oY52fdE.jpeg" style="border-radius:6%; width: 33%">

pandafish-2-7b-32k

pandafish-2-7b-32k is a merge of the following models using LazyMergekit:

💬 Try it

Playground on Huggingface Space

Chat template: Mistral Instruct

⚡ Quantized models

🏆 Evals

ModelAGIEvalGPT4AllTruthfulQABigbenchAverage
🐡 **pandafish-2-7b-32k** 📄40.873.3557.4642.6953.57
Mistral-7B-Instruct-v0.2 📄38.571.6466.8242.2954.81
dolphin-2.8-mistral-7b-v02 📄38.9972.2251.9640.4150.9

🧩 Configuration

yaml
models:
  - model: alpindale/Mistral-7B-v0.2-hf
    # No parameters necessary for base model
  - model: mistralai/Mistral-7B-Instruct-v0.2
    parameters:
      density: 0.53
      weight: 0.4
  - model: cognitivecomputations/dolphin-2.8-mistral-7b-v02
    parameters:
      density: 0.53
      weight: 0.4
merge_method: dare_ties
base_model: alpindale/Mistral-7B-v0.2-hf
parameters:
  int8_mask: true
dtype: bfloat16

💻 Usage

python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "ichigoberry/pandafish-2-7b-32k"
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"])