RichardErkhov/CultriX_-_NeuralTrix-7B-v1-8bits

Quantization made by Richard Erkhov.

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NeuralTrix-7B-v1 - bnb 8bits

• Model creator: https://huggingface.co/CultriX/
• Original model: https://huggingface.co/CultriX/NeuralTrix-7B-v1/

Original model description:

tags: - merge - mergekit - lazymergekit - mlabonne/OmniBeagle-7B - flemmingmiguel/MBX-7B-v3 - AiMavenAi/AiMaven-Prometheus base_model: - mlabonne/OmniBeagle-7B - flemmingmiguel/MBX-7B-v3 - AiMavenAi/AiMaven-Prometheus license: apache-2.0

NeuralTrix-7B-v1

NeuralTrix-7B-v1 is a merge of the following models using LazyMergekit:

• mlabonne/OmniBeagle-7B
• flemmingmiguel/MBX-7B-v3
• AiMavenAi/AiMaven-Prometheus

🧩 Configuration

models:
  - model: mistralai/Mistral-7B-v0.1
    # no parameters necessary for base model
  - model: mlabonne/OmniBeagle-7B
    parameters:
      density: 0.65
      weight: 0.4
  - model: flemmingmiguel/MBX-7B-v3
    parameters:
      density: 0.6
      weight: 0.35
  - model: AiMavenAi/AiMaven-Prometheus
    parameters:
      density: 0.6
      weight: 0.35
merge_method: dare_ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
  int8_mask: true
dtype: float16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "CultriX/NeuralTrix-7B-v1"
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"])