Downloads · 30 days
101
0% of all-time downloads
Kquant03/NeuralTrix-7B-dpo-laser
NeuralTrix-7B-dpo-laser is a text generation model from Kquant03. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.
NeuralTrix-7B-v1 is a merge of the following models using LazyMergekit: mlabonne/OmniBeagle-7B flemmingmiguel/MBX-7B-v3 AiMavenAi/AiMaven-Prometheus
Downloads · 30 days
101
0% of all-time downloads
All-time downloads
20.8K
Public
Repo size
33.5 GB
Likes
6
Public
Click a slice to open those files.
.bin14.5 GB · 100%
From the Hugging Face model README
NeuralTrix-7B-v1 is a merge of the following models using LazyMergekit:
It was then trained with DPO using:
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
!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"])