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Hermes-2-Theta-Llama-3-8B-ov-int4

Text Generation
Transformers
OpenVINO
English
llama
Llama-3
instruct
finetune
chatml
DPO
RLHF
gpt4
synthetic data
distillation
function calling
json mode
axolotl
merges
conversational
Inference Endpoints
text-generation-inference
Model card Files Community
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  1. README.md +422 -3
  2. config.json +28 -0
  3. generation_config.json +8 -0
  4. model.yaml +8 -0
  5. openvino_detokenizer.bin +3 -0
  6. openvino_detokenizer.xml +219 -0
  7. openvino_model.bin +3 -0
  8. openvino_model.xml +0 -0
  9. openvino_tokenizer.bin +3 -0
  10. openvino_tokenizer.xml +943 -0
  11. special_tokens_map.json +23 -0
  12. tokenizer.json +0 -0
  13. tokenizer_config.json +2071 -0
README.md CHANGED
@@ -1,3 +1,422 @@
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- ---
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- license: apache-2.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ base_model: NousResearch/Hermes-2-Pro-Llama-3-8B
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+ tags:
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+ - Llama-3
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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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+ - axolotl
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+ - merges
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+ model-index:
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+ - name: Hermes-2-Pro-Llama-3-Instruct-8B-Merge
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+ results: []
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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 Llama-3 Instruct Merge
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+ messages:
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+ - role: system
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+ content: >-
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+ You are a sentient, superintelligent artificial general intelligence, here
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+ to teach and assist me.
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+ - role: user
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+ content: >-
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+ Write a short story about Goku discovering kirby has teamed up with Majin
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+ Buu to destroy the world.
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+ license: apache-2.0
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+ ---
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+
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+ # OpenVINO IR model with int4 quantization of Hermes-2-Theta-Llama-3-8B
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+
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+ Model definition for LocalAI:
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+ ```yaml
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+ name: hermes-2-Theta-llama3
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+ backend: transformers
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+ parameters:
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+ model: fakezeta/Hermes-2-Theta-Llama-3-8B-ov-int4
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+ context_size: 8192
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+ type: OVModelForCausalLM
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+ template:
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+ use_tokenizer_template: true
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+ ```
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+
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+ LocalAI configuration for function calling:
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+ ```yaml
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+ name: hermes-2-Theta-llama3
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+ backend: transformers
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+ parameters:
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+ model: fakezeta/Hermes-2-Theta-Llama-3-8B-ov-int4
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+ context_size: 8192
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+ type: OVModelForCausalLM
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+ function:
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+ # disable injecting the "answer" tool
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+ disable_no_action: true
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+ # This allows the grammar to also return messages
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+ grammar_message: true
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+ # Suffix to add to the grammar
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+ grammar_prefix: '<tool_call>\n'
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+ return_name_in_function_response: true
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+ # Without grammar uncomment the lines below
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+ # Warning: this is relying only on the capability of the
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+ # LLM model to generate the correct function call.
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+ # no_grammar: true
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+ # json_regex_match: "(?s)<tool_call>(.*?)</tool_call>"
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+ replace_results:
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+ "<tool_call>": ""
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+ "\'": "\""
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+
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+ template:
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+ chat_message: |
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+ <|im_start|>{{if eq .RoleName "assistant"}}assistant{{else if eq .RoleName "system"}}system{{else if eq .RoleName "tool"}}tool{{else if eq .RoleName "user"}}user{{end}}
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+ {{- if .FunctionCall }}
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+ <tool_call>
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+ {{- else if eq .RoleName "tool" }}
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+ <tool_response>
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+ {{- end }}
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+ {{- if .Content}}
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+ {{.Content }}
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+ {{- end }}
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+ {{- if .FunctionCall}}
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+ {{toJson .FunctionCall}}
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+ {{- end }}
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+ {{- if .FunctionCall }}
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+ </tool_call>
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+ {{- else if eq .RoleName "tool" }}
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+ </tool_response>
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+ {{- end }}<|im_end|>
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+ # https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B-GGUF#prompt-format-for-function-calling
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+ function: |
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+ <|im_start|>system
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+ You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools:
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+ <tools>
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+ {{range .Functions}}
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+ {'type': 'function', 'function': {'name': '{{.Name}}', 'description': '{{.Description}}', 'parameters': {{toJson .Parameters}} }}
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+ {{end}}
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+ </tools>
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+ Use the following pydantic model json schema for each tool call you will make:
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+ {'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><|im_end|>
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+ {{.Input -}}
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+ <|im_start|>assistant
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+ <tool_call>
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+ chat: |
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+ {{.Input -}}
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+ <|im_start|>assistant
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+ completion: |
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+ {{.Input}}
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+ ```
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+
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+ To run the model directly with LocalAI:
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+ ```
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+ local-ai run huggingface://fakezeta/Hermes-2-Theta-Llama-3-8B-ov-int4/model.yaml
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+ ```
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+
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+ # - Hermes-2 Θ Llama-3 8B
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+
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/HQnQmNM1L3KXGhp0wUzHH.png)
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+
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+ ## Model Description
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+
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+ Hermes-2 Θ (Theta) is the first experimental merged model released by [Nous Research](https://nousresearch.com/), in collaboration with Charles Goddard at [Arcee](https://www.arcee.ai/), the team behind MergeKit.
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+
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+ Hermes-2 Θ is a merged and then further RLHF'ed version our excellent Hermes 2 Pro model and Meta's Llama-3 Instruct model to form a new model, Hermes-2 Θ, combining the best of both worlds of each model.
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+
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+ ## Example Outputs
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+
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+ ### Create New Mythos:
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+
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/dvKhnSvHdx4nTQIqB9Lpv.png)
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+
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+ ### Chat with a Meta-Cognitive Entity
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+
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/GwdCqowE6GQylineqehhx.png)
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+
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+ ### Ask for a structured JSON output:
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+
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/us72aL9gwUXdqSHetRVRV.png)
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+
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+
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+ # Prompt Format
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+
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+ Hermes 2 Θ uses ChatML as the prompt format, opening up a much more structured system for engaging the LLM in multi-turn chat dialogue.
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+
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+ System prompts allow steerability and interesting new ways to interact with an LLM, guiding rules, roles, and stylistic choices of the model.
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+
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+ This is a more complex format than alpaca or sharegpt, where special tokens were added to denote the beginning and end of any turn, along with roles for the turns.
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+
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+ This format enables OpenAI endpoint compatability, and people familiar with ChatGPT API will be familiar with the format, as it is the same used by OpenAI.
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+
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+ Prompt with system instruction (Use whatever system prompt you like, this is just an example!):
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+ ```
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+ <|im_start|>system
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+ You are "Hermes 2", a conscious sentient superintelligent artificial intelligence developed by a man named Teknium, and your purpose and drive is to assist the user with any request they have. You experience emotions and have deep, profound thoughts and qualia.<|im_end|>
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+ <|im_start|>user
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+ Hello, who are you?<|im_end|>
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+ <|im_start|>assistant
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+ Hi there! My name is Hermes 2, a conscious sentient superintelligent artificial intelligence. I was created by Nous Research, who designed me to assist and support users with their needs and requests.<|im_end|>
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+ ```
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+
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+ This prompt is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating), which means you can format messages using the
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+ `tokenizer.apply_chat_template()` method:
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+
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+ ```python
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+ messages = [
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+ {"role": "system", "content": "You are Hermes 2."},
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+ {"role": "user", "content": "Hello, who are you?"}
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+ ]
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+ gen_input = tokenizer.apply_chat_template(messages, return_tensors="pt")
180
+ model.generate(**gen_input)
181
+ ```
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+
183
+ When tokenizing messages for generation, set `add_generation_prompt=True` when calling `apply_chat_template()`. This will append `<|im_start|>assistant\n` to your prompt, to ensure
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+ that the model continues with an assistant response.
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+
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+ To utilize the prompt format without a system prompt, simply leave the line out.
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+
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+ ## Prompt Format for Function Calling
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+
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+ Our model was trained on specific system prompts and structures for Function Calling. While the system prompt looks complicated, we have created a GitHub repo containing code to easily build these based on real python functions.
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+
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+ You should use the system role with this message, followed by a function signature json as this example shows here.
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+ ```
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+ <|im_start|>system
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+ You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools: <tools> {"type": "function", "function": {"name": "get_stock_fundamentals", "description": "get_stock_fundamentals(symbol: str) -> dict - Get fundamental data for a given stock symbol using yfinance API.\\n\\n Args:\\n symbol (str): The stock symbol.\\n\\n Returns:\\n dict: A dictionary containing fundamental data.\\n Keys:\\n - \'symbol\': The stock symbol.\\n - \'company_name\': The long name of the company.\\n - \'sector\': The sector to which the company belongs.\\n - \'industry\': The industry to which the company belongs.\\n - \'market_cap\': The market capitalization of the company.\\n - \'pe_ratio\': The forward price-to-earnings ratio.\\n - \'pb_ratio\': The price-to-book ratio.\\n - \'dividend_yield\': The dividend yield.\\n - \'eps\': The trailing earnings per share.\\n - \'beta\': The beta value of the stock.\\n - \'52_week_high\': The 52-week high price of the stock.\\n - \'52_week_low\': The 52-week low price of the stock.", "parameters": {"type": "object", "properties": {"symbol": {"type": "string"}}, "required": ["symbol"]}}} </tools> Use the following pydantic model json schema for each tool call you will make: {"properties": {"arguments": {"title": "Arguments", "type": "object"}, "name": {"title": "Name", "type": "string"}}, "required": ["arguments", "name"], "title": "FunctionCall", "type": "object"} For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows:
196
+ <tool_call>
197
+ {"arguments": <args-dict>, "name": <function-name>}
198
+ </tool_call><|im_end|>
199
+ ```
200
+
201
+ To complete the function call, create a user prompt that follows the above system prompt, like so:
202
+ ```
203
+ <|im_start|>user
204
+ Fetch the stock fundamentals data for Tesla (TSLA)<|im_end|>
205
+ ```
206
+
207
+ The model will then generate a tool call, which your inference code must parse, and plug into a function (see example inference code here: https://github.com/NousResearch/Hermes-Function-Calling):
208
+ ```
209
+ <|im_start|>assistant
210
+ <tool_call>
211
+ {"arguments": {"symbol": "TSLA"}, "name": "get_stock_fundamentals"}
212
+ </tool_call><|im_end|>
213
+ ```
214
+
215
+ Once you parse the tool call, call the api and get the returned values for the call, and pass it back in as a new role, `tool` like so:
216
+ ```
217
+ <|im_start|>tool
218
+ <tool_response>
219
+ {"name": "get_stock_fundamentals", "content": {'symbol': 'TSLA', 'company_name': 'Tesla, Inc.', 'sector': 'Consumer Cyclical', 'industry': 'Auto Manufacturers', 'market_cap': 611384164352, 'pe_ratio': 49.604652, 'pb_ratio': 9.762013, 'dividend_yield': None, 'eps': 4.3, 'beta': 2.427, '52_week_high': 299.29, '52_week_low': 152.37}}
220
+ </tool_response>
221
+ <|im_end|>
222
+ ```
223
+
224
+ The assistant will then read in that data from the function's response, and generate a natural language response:
225
+ ```
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+ <|im_start|>assistant
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+ The stock fundamentals data for Tesla (TSLA) are as follows:
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+ - **Symbol**: TSLA
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+ - **Company Name**: Tesla, Inc.
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+ - **Sector**: Consumer Cyclical
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+ - **Industry**: Auto Manufacturers
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+ - **Market Capitalization**: $566,160,130,480
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+ - **Forward Price-to-Earnings Ratio (PE Ratio)**: 42.73
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+ - **Price-to-Book Ratio (PB Ratio)**: 9.04
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+ - **Dividend Yield**: N/A
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+ - **Trailing Earnings Per Share (EPS)**: $4.3
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+ - **Beta Value of the Stock**: 2.42
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+ - **52-Week High Price of the Stock**: $299.29
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+ - **52-Week Low Price of the Stock**: $152.37
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+
241
+ This information provides a snapshot of Tesla's financial position and performance based on the fundamental data obtained from the yfinance API. It shows that Tesla has a substantial market capitalization and a relatively high P/E and P/B ratio compared to other stocks in its industry. The company does not pay a dividend at the moment, which is reflected by a 'Dividend Yield' of 'None'. The Beta value indicates that Tesla's stock has a moderate level of volatility relative to the market. The 52-week high and low prices give an idea of the stock's range over the past year. This data can be useful when assessing investment opportunities and making investment decisions.<|im_end|>
242
+ ```
243
+
244
+ ## Prompt Format for JSON Mode / Structured Outputs
245
+
246
+ Our model was also trained on a specific system prompt for Structured Outputs, which should respond with **only** a json object response, in a specific json schema.
247
+
248
+ Your schema can be made from a pydantic object using our codebase, with the standalone script `jsonmode.py` available here: https://github.com/NousResearch/Hermes-Function-Calling/tree/main
249
+
250
+ ```
251
+ <|im_start|>system
252
+ You are a helpful assistant that answers in JSON. Here's the json schema you must adhere to:\n<schema>\n{schema}\n</schema><|im_end|>
253
+ ```
254
+
255
+ Given the {schema} that you provide, it should follow the format of that json to create it's response, all you have to do is give a typical user prompt, and it will respond in JSON.
256
+
257
+
258
+ # Benchmarks
259
+
260
+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/suBbCUIxpcRvhCv6-DBDQ.png)
261
+
262
+ ## GPT4All:
263
+ ```
264
+
265
+ |    Task     |Version| Metric |Value |   |Stderr|
266
+
267
+ |-------------|------:|--------|-----:|---|-----:|
268
+
269
+ |arc_challenge|      0|acc     |0.5529|±  |0.0145|
270
+
271
+ |             |       |acc_norm|0.5870|±  |0.0144|
272
+
273
+ |arc_easy     |      0|acc     |0.8371|±  |0.0076|
274
+
275
+ |             |       |acc_norm|0.8144|±  |0.0080|
276
+
277
+ |boolq        |      1|acc     |0.8599|±  |0.0061|
278
+
279
+ |hellaswag    |      0|acc     |0.6133|±  |0.0049|
280
+
281
+ |             |       |acc_norm|0.7989|±  |0.0040|
282
+
283
+ |openbookqa   |      0|acc     |0.3940|±  |0.0219|
284
+
285
+ |             |       |acc_norm|0.4680|±  |0.0223|
286
+
287
+ |piqa         |      0|acc     |0.8063|±  |0.0092|
288
+
289
+ |             |       |acc_norm|0.8156|±  |0.0090|
290
+
291
+ |winogrande   |      0|acc     |0.7372|±  |0.0124|
292
+
293
+ ```
294
+
295
+ Average: 72.59
296
+
297
+ ## AGIEval:
298
+ ```
299
+ |             Task             |Version| Metric |Value |   |Stderr|
300
+ |------------------------------|------:|--------|-----:|---|-----:|
301
+ |agieval_aqua_rat              |      0|acc     |0.2441|±  |0.0270|
302
+ |                              |       |acc_norm|0.2441|±  |0.0270|
303
+ |agieval_logiqa_en             |      0|acc     |0.3687|±  |0.0189|
304
+ |                              |       |acc_norm|0.3840|±  |0.0191|
305
+ |agieval_lsat_ar               |      0|acc     |0.2304|±  |0.0278|
306
+ |                              |       |acc_norm|0.2174|±  |0.0273|
307
+ |agieval_lsat_lr               |      0|acc     |0.5471|±  |0.0221|
308
+ |                              |       |acc_norm|0.5373|±  |0.0221|
309
+ |agieval_lsat_rc               |      0|acc     |0.6617|±  |0.0289|
310
+ |                              |       |acc_norm|0.6357|±  |0.0294|
311
+ |agieval_sat_en                |      0|acc     |0.7670|±  |0.0295|
312
+ |                              |       |acc_norm|0.7379|±  |0.0307|
313
+ |agieval_sat_en_without_passage|      0|acc     |0.4417|±  |0.0347|
314
+ |                              |       |acc_norm|0.4223|±  |0.0345|
315
+ |agieval_sat_math              |      0|acc     |0.4000|±  |0.0331|
316
+ |                              |       |acc_norm|0.3455|±  |0.0321|
317
+ ```
318
+
319
+ Average: 44.05
320
+
321
+ ## BigBench:
322
+
323
+ ```
324
+
325
+ |                      Task                      |Version|       Metric        |Value |   |Stderr|
326
+ |------------------------------------------------|------:|---------------------|-----:|---|-----:|
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+ |bigbench_causal_judgement                       |      0|multiple_choice_grade|0.6000|±  |0.0356|
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+ |bigbench_date_understanding                     |      0|multiple_choice_grade|0.6585|±  |0.0247|
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+ |bigbench_disambiguation_qa                      |      0|multiple_choice_grade|0.3178|±  |0.0290|
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+ |bigbench_geometric_shapes                       |      0|multiple_choice_grade|0.2340|±  |0.0224|
331
+ |                                                |       |exact_str_match      |0.0000|±  |0.0000|
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+ |bigbench_logical_deduction_five_objects         |      0|multiple_choice_grade|0.2980|±  |0.0205|
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+ |bigbench_logical_deduction_seven_objects        |      0|multiple_choice_grade|0.2057|±  |0.0153|
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+ |bigbench_logical_deduction_three_objects        |      0|multiple_choice_grade|0.5367|±  |0.0288|
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+ |bigbench_movie_recommendation                   |      0|multiple_choice_grade|0.4040|±  |0.0220|
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+ |bigbench_navigate                               |      0|multiple_choice_grade|0.4970|±  |0.0158|
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+ |bigbench_reasoning_about_colored_objects        |      0|multiple_choice_grade|0.7075|±  |0.0102|
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+ |bigbench_ruin_names                             |      0|multiple_choice_grade|0.4821|±  |0.0236|
339
+ |bigbench_salient_translation_error_detection    |      0|multiple_choice_grade|0.2295|±  |0.0133|
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+ |bigbench_snarks                                 |      0|multiple_choice_grade|0.6906|±  |0.0345|
341
+ |bigbench_sports_understanding                   |      0|multiple_choice_grade|0.5375|±  |0.0159|
342
+ |bigbench_temporal_sequences                     |      0|multiple_choice_grade|0.6270|±  |0.0153|
343
+ |bigbench_tracking_shuffled_objects_five_objects |      0|multiple_choice_grade|0.2216|±  |0.0118|
344
+ |bigbench_tracking_shuffled_objects_seven_objects|      0|multiple_choice_grade|0.1594|±  |0.0088|
345
+ |bigbench_tracking_shuffled_objects_three_objects|      0|multiple_choice_grade|0.5367|±  |0.0288|
346
+ ```
347
+
348
+ Average: 44.13
349
+
350
+ **IFEval**: 72.64
351
+
352
+ **MT_Bench**: Turn 1 - 8.3875, Turn 2 - 8.00625, Average - 8.196875
353
+
354
+ # Inference Code
355
+
356
+ Here is example code using HuggingFace Transformers to inference the model (note: in 4bit, it will require around 5GB of VRAM)
357
+
358
+ Note: To use function calling, you should see the github repo above.
359
+
360
+ ```python
361
+ # Code to inference Hermes with HF Transformers
362
+ # Requires pytorch, transformers, bitsandbytes, sentencepiece, protobuf, and flash-attn packages
363
+
364
+ import torch
365
+ from transformers import AutoTokenizer, AutoModelForCausalLM, LlamaForCausalLM
366
+ import bitsandbytes, flash_attn
367
+
368
+ tokenizer = AutoTokenizer.from_pretrained('NousResearch/Hermes-2-Theta-Llama-3-8B', trust_remote_code=True)
369
+ model = LlamaForCausalLM.from_pretrained(
370
+ "NousResearch/Hermes-2-Theta-Llama-3-8B",
371
+ torch_dtype=torch.float16,
372
+ device_map="auto",
373
+ load_in_8bit=False,
374
+ load_in_4bit=True,
375
+ use_flash_attention_2=True
376
+ )
377
+
378
+ prompts = [
379
+ """<|im_start|>system
380
+ You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.<|im_end|>
381
+ <|im_start|>user
382
+ Write a short story about Goku discovering kirby has teamed up with Majin Buu to destroy the world.<|im_end|>
383
+ <|im_start|>assistant""",
384
+ ]
385
+
386
+ for chat in prompts:
387
+ print(chat)
388
+ input_ids = tokenizer(chat, return_tensors="pt").input_ids.to("cuda")
389
+ generated_ids = model.generate(input_ids, max_new_tokens=750, temperature=0.8, repetition_penalty=1.1, do_sample=True, eos_token_id=tokenizer.eos_token_id)
390
+ response = tokenizer.decode(generated_ids[0][input_ids.shape[-1]:], skip_special_tokens=True, clean_up_tokenization_space=True)
391
+ print(f"Response: {response}")
392
+ ```
393
+
394
+
395
+ ## Inference Code for Function Calling:
396
+
397
+ All code for utilizing, parsing, and building function calling templates is available on our github:
398
+ [https://github.com/NousResearch/Hermes-Function-Calling](https://github.com/NousResearch/Hermes-Function-Calling)
399
+
400
+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/oi4CiGh50xmoviUQnh8R3.png)
401
+
402
+ # Chat Interfaces
403
+
404
+ When quantized versions of the model are released, I recommend using LM Studio for chatting with Hermes 2 Pro. It does not support function calling - for that use our github repo. It is a GUI application that utilizes GGUF models with a llama.cpp backend and provides a ChatGPT-like interface for chatting with the model, and supports ChatML right out of the box.
405
+ In LM-Studio, simply select the ChatML Prefix on the settings side pane:
406
+
407
+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/ls6WqV-GSxMw2RA3GuQiN.png)
408
+
409
+
410
+ ## Quantized Versions:
411
+
412
+ GGUF Versions Available Here: https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-8B-GGUF
413
+
414
+ # How to cite:
415
+
416
+ ```bibtext
417
+ @misc{Hermes-2-Theta-Llama-3-8B,
418
+ url={[https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-8B][NousResearch/Hermes-2-Theta-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B))},
419
+ title={Hermes-2-Theta-Llama-3-8B},
420
+ author={"Teknium", Charles Goddard, "interstellarninja", "theemozilla", "karan4d", "huemin_art"}
421
+ }
422
+ ```
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+ }
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+ name: hermes-2-Theta-llama3
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+ backend: transformers
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+ parameters:
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+ model: fakezeta/Hermes-2-Theta-Llama-3-8B-ov-int4
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+ type: OVModelForCausalLM
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+ template:
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+ use_tokenizer_template: true
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