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RichardErkhov/
saucam_-_Nereus-7B-gguf

Name: saucam_-_Nereus-7B-gguf
Availability: InStock

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Nereus-7B - GGUF

Model creator: https://huggingface.co/saucam/
Original model: https://huggingface.co/saucam/Nereus-7B/

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

Original model description:

tags:

merge
mergekit
cognitivecomputations/dolphin-2.8-mistral-7b-v02
NousResearch/Hermes-2-Pro-Mistral-7B base_model:
cognitivecomputations/dolphin-2.8-mistral-7b-v02 license: apache-2.0

🌊 Nereus-7B

Nereus-7B excels at conversations, coding, and tasks that require structured output in JSON. It is a merge of the following models using mergekit:

🧩 Configuration

yamlmodels:

  - model: cognitivecomputations/dolphin-2.8-mistral-7b-v02
    parameters:
      density: 0.5
      weight: 0.4
    # No parameters necessary for base model
  - model: NousResearch/Hermes-2-Pro-Mistral-7B
    parameters:
      density: 0.5
      weight: 0.6
merge_method: dare_ties
base_model: cognitivecomputations/dolphin-2.8-mistral-7b-v02
parameters:
  int8_mask: true
dtype: bfloat16

Eval Results

Benchmark	Model	agieval	gpt4all	bigbench	truthfulqa	Average
nous	Nereus-7B	42.8	72.21	39.17	54.32	52.12

Benchmark	Model	winogrande	arc	gsm8k	mmlu	truthfulqa	hellaswag	Average
openllm	Nereus-7B	76.95	62.54	46.25	59.6	54.32	83.23	63.82

For detailed results see here

💻 Usage

python

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "saucam/Nereus-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"])

Sample responses

What is a large language model?<|im_end|>
<|im_start|>assistant
A large language model is a type of artificial intelligence (AI) system that has been trained on vast amounts of text data to understand, generate, and predict patterns in human language. It is designed to process and analyze natural language input, making it capable of tasks such as text generation, translation, language translation, and text classification. These models are typically based on deep learning techniques, particularly neural networks, and are trained on large datasets, often consisting of billions of words. Some well-known large language models include GPT-3 by OpenAI, BERT by Google, and T5 by Google. These models can be fine-tuned for specific tasks or domains to improve their performance. They have revolutionized the field of natural language processing and have numerous applications in areas such as chatbots, search engines, and automated writing assistance.

RichardErkhov/saucam_-_Nereus-7B-gguf