metadata

base_model: PocketDoc/Dans-PersonalityEngine-v1.0.0-8b
datasets:
  - PocketDoc/Dans-MemoryCore-CoreCurriculum-Small
  - PocketDoc/Dans-Prosemaxx-Gutenberg
  - PocketDoc/Dans-Prosemaxx-Cowriter-S
  - PocketDoc/Dans-Prosemaxx-Adventure
  - PocketDoc/Dans-Prosemaxx-Opus-Writing
  - PocketDoc/Dans-Assistantmaxx-Sharegpt
  - PocketDoc/Dans-Assistantmaxx-OpenAssistant2
  - PocketDoc/Dans-Assistantmaxx-Opus-instruct-1
  - PocketDoc/Dans-Assistantmaxx-Opus-instruct-2
  - PocketDoc/Dans-Assistantmaxx-Opus-instruct-3
  - PocketDoc/Dans-Assistantmaxx-Opus-Multi-Instruct
  - PocketDoc/Dans-Assistantmaxx-sonnetorca-subset
  - PocketDoc/Dans-Assistantmaxx-NoRobots
  - AquaV/Energetic-Materials-Sharegpt
  - AquaV/Chemical-Biological-Safety-Applications-Sharegpt
  - AquaV/US-Army-Survival-Sharegpt
  - AquaV/Resistance-Sharegpt
  - AquaV/Interrogation-Sharegpt
  - AquaV/Multi-Environment-Operations-Sharegpt
  - PocketDoc/Dans-Mathmaxx
  - PJMixers/Math-Multiturn-1K-ShareGPT
  - PocketDoc/Dans-Benchmaxx
  - PocketDoc/Dans-Codemaxx-LeetCode
  - PocketDoc/Dans-Codemaxx-CodeFeedback-Conversations
  - PocketDoc/Dans-Codemaxx-CodeFeedback-SingleTurn
  - PocketDoc/Dans-Taskmaxx
  - PocketDoc/Dans-Taskmaxx-DataPrepper
  - PocketDoc/Dans-Taskmaxx-ConcurrentQA-Reworked
  - PocketDoc/Dans-Systemmaxx
  - PocketDoc/Dans-Toolmaxx-Agent
  - PocketDoc/Dans-Toolmaxx-ShellCommands
  - PocketDoc/Dans-ASCIIMaxx-Wordart
  - PocketDoc/Dans-Personamaxx
  - PocketDoc/DansTestYard
  - PocketDoc/Dans-Logicmaxx-Skunkworks
language:
  - en
license: apache-2.0
pipeline_tag: text-generation
tags:
  - chemistry
  - biology
  - code
  - climate
  - text-generation-inference
  - llama-cpp
  - gguf-my-repo

Triangle104/Dans-PersonalityEngine-v1.0.0-8b-Q4_K_M-GGUF

This model was converted to GGUF format from PocketDoc/Dans-PersonalityEngine-v1.0.0-8b using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Model details:

What is it?

This model is intended to be multifarious in its capabilities and should be quite capable at both co-writing and roleplay as well as find itself quite at home performing sentiment analysis or summarization as part of a pipeline. It has been trained on a wide array of one shot instructions, multi turn instructions, role playing scenarios, text adventure games, co-writing, and much more. The full dataset is publicly available and can be found in the datasets section of the model page.

There has not been any form of harmfulness alignment done on this model, please take the appropriate precautions when using it in a production environment. Prompting

The model has been trained on standard "ChatML" format prompting, an example of which is shown below:

SillyTavern templates

Below are Instruct and Context templates for use within SillyTavern. context template

{ "story_string": "<|im_start|>system\n{{#if system}}{{system}}\n{{/if}}{{#if wiBefore}}{{wiBefore}}\n{{/if}}{{#if description}}{{description}}\n{{/if}}{{#if personality}}{{char}}'s personality: {{personality}}\n{{/if}}{{#if scenario}}Scenario: {{scenario}}\n{{/if}}{{#if wiAfter}}{{wiAfter}}\n{{/if}}{{#if persona}}{{persona}}\n{{/if}}{{trim}}<|im_end|>\n", "example_separator": "", "chat_start": "", "use_stop_strings": false, "allow_jailbreak": false, "always_force_name2": false, "trim_sentences": false, "include_newline": false, "single_line": false, "name": "Dan-ChatML" }

instruct template

{ "system_prompt": "Write {{char}}'s actions and dialogue, user will write {{user}}'s.", "input_sequence": "<|im_start|>user\n", "output_sequence": "<|im_start|>assistant\n", "first_output_sequence": "", "last_output_sequence": "", "system_sequence_prefix": "", "system_sequence_suffix": "", "stop_sequence": "<|im_end|>", "wrap": false, "macro": true, "names": false, "names_force_groups": false, "activation_regex": "", "skip_examples": false, "output_suffix": "<|im_end|>\n", "input_suffix": "<|im_end|>\n", "system_sequence": "<|im_start|>system\n", "system_suffix": "<|im_end|>\n", "user_alignment_message": "", "last_system_sequence": "", "system_same_as_user": false, "first_input_sequence": "", "last_input_sequence": "", "name": "Dan-ChatML" }

Training

This model was full finetuned for 4 epochs on 8x H100 equating to 21 hours.

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Triangle104/Dans-PersonalityEngine-v1.0.0-8b-Q4_K_M-GGUF --hf-file dans-personalityengine-v1.0.0-8b-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Dans-PersonalityEngine-v1.0.0-8b-Q4_K_M-GGUF --hf-file dans-personalityengine-v1.0.0-8b-q4_k_m.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo Triangle104/Dans-PersonalityEngine-v1.0.0-8b-Q4_K_M-GGUF --hf-file dans-personalityengine-v1.0.0-8b-q4_k_m.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo Triangle104/Dans-PersonalityEngine-v1.0.0-8b-Q4_K_M-GGUF --hf-file dans-personalityengine-v1.0.0-8b-q4_k_m.gguf -c 2048