
# Proximus-2x7B-v1

Proximus-2x7B-v1 is a Mixure of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [beowolx/MistralHermes-CodePro-7B-v1](https://huggingface.co/beowolx/MistralHermes-CodePro-7B-v1)
* [anthonylx/Prox-MistralHermes-7B](https://huggingface.co/anthonylx/Prox-MistralHermes-7B)

## 🧩 Configuration

```yaml
base_model: mlabonne/NeuralHermes-2.5-Mistral-7B
experts:
  - source_model: beowolx/MistralHermes-CodePro-7B-v1
    positive_prompts:
      - "code"
      - "python"
      - "javascript"
      - "programming"
      - "algorithm"
  - source_model: anthonylx/Prox-MistralHermes-7B
    positive_prompts:
      - "cybersecurity"
      - "information security"
      - "network security"
      - "hacking"
      - "encryption"
```

## 💻 Usage

```python
!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "anthonylx/Proximus-2x7B-v1"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)

messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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"])
```
{"license": "apache-2.0", "tags": ["moe", "merge", "mergekit", "lazymergekit", "beowolx/MistralHermes-CodePro-7B-v1", "anthonylx/Prox-MistralHermes-7B"]}
transformers safetensors mixtral text-generation moe merge mergekit lazymergekit beowolx/MistralHermes-CodePro-7B-v1 anthonylx/Prox-MistralHermes-7B conversational license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us