---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
tags:
- serialization
---
![image/png](https://cdn-uploads.huggingface.co/production/uploads/63fe1a380c1bbe8e29d3c401/lLSAHJVQKuEqKCgFIMEsY.png)


# Model Card for Neural-Zephyr Mistral 14B

Intel and Hugging Face developed two of the most prominent Mistral-type models released: Neural-Chat and Zephyr.

Neural-Zephyr is a hybrid Transfer Learning version joining Neural-Chat weights and Zephyr Mistral type models. The weights are aggregated in the same layers, summing up 14B parameters.

Zephyr is a series of language models that are trained to act as helpful assistants. 
Zephyr-7B-β is the second model in the series, and is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
that was trained on a mix of publicly available, synthetic datasets using [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290). 
and made the model more helpful. However, this means that model is likely to generate problematic text when prompted to do so. 
You can find more details in the [technical report](https://arxiv.org/abs/2310.16944).


## Model description

- **Model type:** A 14B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets.
- **Language(s) (NLP):** Primarily English
- **License:** MIT
- **Finetuned from model:** [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)


## Use in Transformers
**Load model directly** \
import torch \
from transformers import AutoTokenizer, AutoModelForCausalLM, MistralForCausalLM \
from huggingface_hub import hf_hub_download

model = MistralForCausalLM.from_pretrained("ai-agi/neural-zephyr", use_cache=False,  torch_dtype=torch.bfloat16, device_map="auto") \
model_weights = hf_hub_download(repo_id="ai-agi/neural-zephyr", filename="model_weights.pth") \
state_dict = torch.load(model_weights) \
model.load_state_dict(state_dict)

tokenizer = AutoTokenizer.from_pretrained("ai-agi/neural-zephyr", use_fast=True) \
if tokenizer.pad_token is None: \
    tokenizer.pad_token = tokenizer.eos_token \
**Manage your GPU/CPU memory for model and weights**