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Browse files- README.md +71 -0
- config.json +27 -0
- linear_config.yaml +12 -0
- mergekit_config.yml +12 -0
- model-00001-of-00015.safetensors +3 -0
- model-00002-of-00015.safetensors +3 -0
- model-00003-of-00015.safetensors +3 -0
- model-00004-of-00015.safetensors +3 -0
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- model-00011-of-00015.safetensors +3 -0
- model-00012-of-00015.safetensors +3 -0
- model-00013-of-00015.safetensors +3 -0
- model-00014-of-00015.safetensors +3 -0
- model-00015-of-00015.safetensors +3 -0
- model.safetensors.index.json +1 -0
- special_tokens_map.json +23 -0
- tokenizer.json +0 -0
- tokenizer.model +3 -0
- tokenizer_config.json +0 -0
README.md
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---
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license: apache-2.0
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tags:
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- merge
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- mergekit
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- lazymergekit
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- NousResearch/Hermes-2-Pro-Mistral-7B
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- mistralai/Mistral-7B-Instruct-v0.3
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---
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# Hermes-2-Pro-Mistral-7B-Mistral-7B-Instruct-v0.3-linear-merge
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Hermes-2-Pro-Mistral-7B-Mistral-7B-Instruct-v0.3-linear-merge is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
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* [NousResearch/Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B)
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* [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3)
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## 🧩 Merge Configuration
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```yaml
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merge_method: linear
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base_model: mistralai/Mistral-7B-Instruct-v0.3
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models:
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- model: NousResearch/Hermes-2-Pro-Mistral-7B
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parameters:
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weight: 0.3
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- model: mistralai/Mistral-7B-Instruct-v0.3
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parameters:
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weight: 0.7
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parameters:
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normalize: true
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dtype: float16
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```
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## Model Description
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The Hermes-2-Pro-Mistral-7B-Mistral-7B-Instruct-v0.3-linear-merge combines the advanced conversational capabilities of the Hermes 2 Pro model with the instruction-following prowess of the Mistral-7B-Instruct model. This strategic fusion aims to enhance the model's ability to understand and generate contextually relevant responses while maintaining a high level of performance across various natural language processing tasks.
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Hermes 2 Pro is an upgraded version of the original Nous Hermes 2, featuring a refined dataset and improved function calling capabilities. It excels in generating structured outputs, making it particularly useful for applications requiring precise data formatting, such as JSON responses. The Mistral-7B-Instruct model, on the other hand, is designed to follow instructions effectively, making it a strong candidate for tasks that require adherence to user prompts.
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## Use Cases
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This merged model is well-suited for a variety of applications, including but not limited to:
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- Conversational agents and chatbots
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- Function calling and structured data generation
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- Instruction-based tasks and question answering
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- Creative writing and storytelling
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## Model Features
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- **Enhanced Conversational Abilities**: The model leverages the conversational strengths of Hermes 2 Pro, allowing for engaging and context-aware dialogues.
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- **Instruction Following**: With the integration of Mistral-7B-Instruct, the model can effectively follow user instructions, making it ideal for task-oriented applications.
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- **Function Calling and JSON Outputs**: The model supports advanced function calling and can generate structured JSON outputs, facilitating integration with various applications and APIs.
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## Evaluation Results
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The performance of the parent models provides a solid foundation for the merged model. Here are some evaluation metrics from the original models:
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### Hermes 2 Pro
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- **Function Calling Accuracy**: 91%
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- **JSON Mode Accuracy**: 84%
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### Mistral-7B-Instruct
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While specific evaluation metrics for Mistral-7B-Instruct were not available, it is known for its strong instruction-following capabilities, which contribute to the overall performance of the merged model.
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## Limitations
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Despite the strengths of the merged model, it may inherit some limitations from its parent models. Potential issues include:
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- **Biases**: The model may reflect biases present in the training data of both parent models, which could affect the fairness and neutrality of its outputs.
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- **Contextual Understanding**: While the model excels in many areas, there may still be challenges in understanding highly nuanced or ambiguous prompts.
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In summary, the Hermes-2-Pro-Mistral-7B-Mistral-7B-Instruct-v0.3-linear-merge represents a powerful tool for a wide range of NLP tasks, combining the best features of its parent models while also carrying forward some of their limitations.
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config.json
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{
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"_name_or_path": "mistralai/Mistral-7B-Instruct-v0.3",
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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": 2,
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"head_dim": 128,
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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": "float16",
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"transformers_version": "4.44.0",
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"use_cache": true,
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"vocab_size": 32768
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}
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linear_config.yaml
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merge_method: linear
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base_model: mistralai/Mistral-7B-Instruct-v0.3
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models:
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- model: NousResearch/Hermes-2-Pro-Mistral-7B
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parameters:
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weight: 0.3
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- model: mistralai/Mistral-7B-Instruct-v0.3
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parameters:
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weight: 0.7
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parameters:
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normalize: true
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dtype: float16
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mergekit_config.yml
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merge_method: linear
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base_model: mistralai/Mistral-7B-Instruct-v0.3
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models:
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- model: NousResearch/Hermes-2-Pro-Mistral-7B
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parameters:
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weight: 0.3
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- model: mistralai/Mistral-7B-Instruct-v0.3
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parameters:
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weight: 0.7
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parameters:
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normalize: true
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dtype: float16
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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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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"eos_token": {
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"content": "</s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"unk_token": {
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"content": "<unk>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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}
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tokenizer.json
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tokenizer.model
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version https://git-lfs.github.com/spec/v1
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oid sha256:37f00374dea48658ee8f5d0f21895b9bc55cb0103939607c8185bfd1c6ca1f89
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size 587404
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tokenizer_config.json
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