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Model: solidrust/Hermes-2-Pro-Mistral-7B-AWQ Source: Original Platform
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README.md
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README.md
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---
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base_model: NousResearch/Hermes-2-Pro-Mistral-7B
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tags:
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- Mistral
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- instruct
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- finetune
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- chatml
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- DPO
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- RLHF
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- gpt4
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- synthetic data
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- distillation
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- function calling
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- json mode
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- quantized
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- 4-bit
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- AWQ
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- text-generation
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- autotrain_compatible
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- endpoints_compatible
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- chatml
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model-index:
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- name: Hermes-2-Pro-Mistral-7B
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results: []
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license: apache-2.0
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language:
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- en
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datasets:
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- teknium/OpenHermes-2.5
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widget:
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- example_title: Hermes 2 Pro
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messages:
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- role: system
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content: You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.
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- role: user
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content: Write a short story about Goku discovering kirby has teamed up with Majin Buu to destroy the world.
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model_type: mistral
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pipeline_tag: text-generation
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inference: false
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prompt_template: '<|im_start|>system
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{system_message}<|im_end|>
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<|im_start|>user
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{prompt}<|im_end|>
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<|im_start|>assistant
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'
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quantized_by: Suparious
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---
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# NousResearch/Hermes-2-Pro-Mistral-7B AWQ
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- Model creator: [NousResearch](https://huggingface.co/NousResearch)
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- Original model: [Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B)
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## Model Summary
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Hermes 2 Pro on Mistral 7B is the new flagship 7B Hermes!
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Hermes 2 Pro is an upgraded, retrained version of Nous Hermes 2, consisting of an updated and cleaned version of the OpenHermes 2.5 Dataset, as well as a newly introduced Function Calling and JSON Mode dataset developed in-house.
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This new version of Hermes maintains its excellent general task and conversation capabilities - but also excels at Function Calling, JSON Structured Outputs, and has improved on several other metrics as well, scoring a 90% on our function calling evaluation built in partnership with Fireworks.AI, and an 84% on our structured JSON Output evaluation.
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Hermes Pro takes advantage of a special system prompt and multi-turn function calling structure with a new chatml role in order to make function calling reliable and easy to parse. Learn more about prompting below.
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This work was a collaboration between Nous Research, @interstellarninja, and Fireworks.AI
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Learn more about the function calling system for this model on our github repo here: https://github.com/NousResearch/Hermes-Function-Calling
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## How to use
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### Install the necessary packages
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```bash
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pip install --upgrade autoawq autoawq-kernels
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```
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### Example Python code
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```python
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from awq import AutoAWQForCausalLM
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from transformers import AutoTokenizer, TextStreamer
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model_path = "solidrust/Hermes-2-Pro-Mistral-7B-AWQ"
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system_message = "You are Hermes, incarnated as a powerful AI."
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# Load model
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model = AutoAWQForCausalLM.from_quantized(model_path,
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fuse_layers=True)
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tokenizer = AutoTokenizer.from_pretrained(model_path,
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trust_remote_code=True)
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streamer = TextStreamer(tokenizer,
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skip_prompt=True,
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skip_special_tokens=True)
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# Convert prompt to tokens
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prompt_template = """\
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<|im_start|>system
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{system_message}<|im_end|>
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<|im_start|>user
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{prompt}<|im_end|>
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<|im_start|>assistant"""
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prompt = "You're standing on the surface of the Earth. "\
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"You walk one mile south, one mile west and one mile north. "\
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"You end up exactly where you started. Where are you?"
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tokens = tokenizer(prompt_template.format(system_message=system_message,prompt=prompt),
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return_tensors='pt').input_ids.cuda()
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# Generate output
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generation_output = model.generate(tokens,
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streamer=streamer,
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max_new_tokens=512)
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```
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### About AWQ
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AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
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AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
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It is supported by:
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- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
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- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
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- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
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- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
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- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
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## Prompt template: ChatML
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```plaintext
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<|im_start|>system
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{system_message}<|im_end|>
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<|im_start|>user
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{prompt}<|im_end|>
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<|im_start|>assistant
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```
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## Function calling
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System prompt example:
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```plaintext
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You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags.
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You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions.
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Use the following json schema for each tool call you will make: {"title": "FunctionCall", "type": "object", "properties": {"arguments": {"title": "Arguments", "type": "object"}, "name": {"title": "Name", "type": "string"}}, "required": ["arguments", "name"]}
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For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows:
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<tool_call>
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{"arguments": <args-dict>, "name": <function-name>}
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</tool_call>
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```
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## How to cite:
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```plaintext
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@misc{Hermes-2-Pro-Mistral-7B,
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url={[https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B]https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B)},
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title={Hermes-2-Pro-Mistral-7B},
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author={"interstellarninja", "Teknium", "theemozilla", "karan4d", "huemin_art"}
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}
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```
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