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Model: saucam/Nereus-7B Source: Original Platform
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README.md
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---
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tags:
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- merge
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- mergekit
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- cognitivecomputations/dolphin-2.8-mistral-7b-v02
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- NousResearch/Hermes-2-Pro-Mistral-7B
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base_model:
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- cognitivecomputations/dolphin-2.8-mistral-7b-v02
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license: apache-2.0
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---
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# 🌊 Nereus-7B
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Nereus-7B excels at conversations, coding, and tasks that require structured output in JSON.
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It is a merge of the following models using [mergekit](https://github.com/arcee-ai/mergekit):
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* [cognitivecomputations/dolphin-2.8-mistral-7b-v02](https://huggingface.co/cognitivecomputations/dolphin-2.8-mistral-7b-v02)
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* [NousResearch/Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B)
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## 🧩 Configuration
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```yamlmodels:
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- model: cognitivecomputations/dolphin-2.8-mistral-7b-v02
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parameters:
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density: 0.5
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weight: 0.4
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# No parameters necessary for base model
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- model: NousResearch/Hermes-2-Pro-Mistral-7B
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parameters:
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density: 0.5
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weight: 0.6
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merge_method: dare_ties
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base_model: cognitivecomputations/dolphin-2.8-mistral-7b-v02
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parameters:
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int8_mask: true
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dtype: bfloat16
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```
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## Eval Results
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|Benchmark| Model |agieval|gpt4all|bigbench|truthfulqa|Average|
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|---------|----------------------------------------------------|------:|------:|-------:|---------:|------:|
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|nous |[Nereus-7B](https://huggingface.co/saucam/Nereus-7B)| 42.8| 72.21| 39.17| 54.32| 52.12|
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|Benchmark| Model |winogrande| arc |gsm8k|mmlu|truthfulqa|hellaswag|Average|
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|---------|----------------------------------------------------|---------:|----:|----:|---:|---------:|--------:|------:|
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|openllm |[Nereus-7B](https://huggingface.co/saucam/Nereus-7B)| 76.95|62.54|46.25|59.6| 54.32| 83.23| 63.82|
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For detailed results [see here](https://github.com/saucam/model_evals/blob/main/saucam/Nereus-7B/README.md)
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## 💻 Usage
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```python
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!pip install -qU transformers accelerate
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from transformers import AutoTokenizer
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import transformers
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import torch
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model = "saucam/Nereus-7B"
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messages = [{"role": "user", "content": "What is a large language model?"}]
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tokenizer = AutoTokenizer.from_pretrained(model)
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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pipeline = transformers.pipeline(
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"text-generation",
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model=model,
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torch_dtype=torch.float16,
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device_map="auto",
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)
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outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
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print(outputs[0]["generated_text"])
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```
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Sample responses
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```
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What is a large language model?<|im_end|>
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<|im_start|>assistant
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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.
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```
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added_tokens.json
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{
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"<|im_end|>": 32000,
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"<|im_start|>": 32001
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}
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config.json
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config.json
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{
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"_name_or_path": "cognitivecomputations/dolphin-2.8-mistral-7b-v02",
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"architectures": [
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"MistralForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"eos_token_id": 32000,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 32768,
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"model_type": "mistral",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"rms_norm_eps": 1e-05,
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"rope_theta": 1000000.0,
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"sliding_window": null,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.39.3",
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"use_cache": false,
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"vocab_size": 32002
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}
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mergekit_config.yml
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mergekit_config.yml
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models:
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- model: cognitivecomputations/dolphin-2.8-mistral-7b-v02
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parameters:
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density: 0.5
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weight: 0.4
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# No parameters necessary for base model
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- model: NousResearch/Hermes-2-Pro-Mistral-7B
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parameters:
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density: 0.5
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weight: 0.6
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merge_method: dare_ties
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base_model: cognitivecomputations/dolphin-2.8-mistral-7b-v02
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parameters:
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int8_mask: true
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dtype: bfloat16
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model-00001-of-00002.safetensors
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special_tokens_map.json
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{
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"bos_token": {
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"content": "<s>",
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"eos_token": {
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"content": "<|im_end|>",
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}
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tokenizer.json
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tokenizer.json
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tokenizer.model
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tokenizer_config.json
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{
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"add_bos_token": true,
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"add_eos_token": false,
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"add_prefix_space": true,
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"32000": {
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"content": "<|im_end|>",
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"special": false
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}
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},
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"bos_token": "<s>",
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"chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"legacy": true,
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"pad_token": "</s>",
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"sp_model_kwargs": {},
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"spaces_between_special_tokens": false,
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"tokenizer_class": "LlamaTokenizer",
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"unk_token": "<unk>",
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"use_default_system_prompt": false,
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"use_fast": true
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}
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