Instructions to use OwenArli/Llama-3.1-70B-ArliAI-RPMax-v1.2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use OwenArli/Llama-3.1-70B-ArliAI-RPMax-v1.2-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf OwenArli/Llama-3.1-70B-ArliAI-RPMax-v1.2-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf OwenArli/Llama-3.1-70B-ArliAI-RPMax-v1.2-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf OwenArli/Llama-3.1-70B-ArliAI-RPMax-v1.2-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf OwenArli/Llama-3.1-70B-ArliAI-RPMax-v1.2-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf OwenArli/Llama-3.1-70B-ArliAI-RPMax-v1.2-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf OwenArli/Llama-3.1-70B-ArliAI-RPMax-v1.2-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf OwenArli/Llama-3.1-70B-ArliAI-RPMax-v1.2-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf OwenArli/Llama-3.1-70B-ArliAI-RPMax-v1.2-GGUF:Q4_K_M
Use Docker
docker model run hf.co/OwenArli/Llama-3.1-70B-ArliAI-RPMax-v1.2-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use OwenArli/Llama-3.1-70B-ArliAI-RPMax-v1.2-GGUF with Ollama:
ollama run hf.co/OwenArli/Llama-3.1-70B-ArliAI-RPMax-v1.2-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use OwenArli/Llama-3.1-70B-ArliAI-RPMax-v1.2-GGUF with Docker Model Runner:
docker model run hf.co/OwenArli/Llama-3.1-70B-ArliAI-RPMax-v1.2-GGUF:Q4_K_M
- Lemonade
How to use OwenArli/Llama-3.1-70B-ArliAI-RPMax-v1.2-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull OwenArli/Llama-3.1-70B-ArliAI-RPMax-v1.2-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Llama-3.1-70B-ArliAI-RPMax-v1.2-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Llama-3.1-70B-ArliAI-RPMax-v1.2
=====================================
RPMax Series Overview
| 2B | 3.8B | 8B | 9B | 12B | 20B | 22B | 70B |
RPMax is a series of models that are trained on a diverse set of curated creative writing and RP datasets with a focus on variety and deduplication. This model is designed to be highly creative and non-repetitive by making sure no two entries in the dataset have repeated characters or situations, which makes sure the model does not latch on to a certain personality and be capable of understanding and acting appropriately to any characters or situations.
Early tests by users mentioned that these models does not feel like any other RP models, having a different style and generally doesn't feel in-bred.
You can access the model at https://arliai.com and ask questions at https://www.reddit.com/r/ArliAI/
We also have a models ranking page at https://www.arliai.com/models-ranking
Ask questions in our new Discord Server! https://discord.com/invite/t75KbPgwhk
Model Description
Llama-3.1-70B-ArliAI-RPMax-v1.2 is a variant made from the Meta-Llama-3.1-70B-Instruct model.
Let us know what you think of the model! The 8B and 12B versions of RPMax had great feedback from users, so we expect this 70B version to one of the best RP models.
v1.2 update completely removes non-creative/RP examples in the dataset and is also an incremental improvement of the RPMax dataset which dedups the dataset even more and better filtering to cutout irrelevant description text that came from card sharing sites.
Specs
- Context Length: 128K
- Parameters: 70B
Training Details
- Sequence Length: 4096
- Training Duration: Approximately 5 days on 2x3090Ti
- Epochs: 1 epoch training for minimized repetition sickness
- LORA: 64-rank 128-alpha, resulting in ~2% trainable weights
- Learning Rate: 0.00001
- Gradient accumulation: Very low 32 for better learning.
Quantization
The model is available in quantized formats:
- FP16: https://huggingface.co/ArliAI/Llama-3.1-70B-ArliAI-RPMax-v1.2
- GGUF: https://huggingface.co/ArliAI/Llama-3.1-70B-ArliAI-RPMax-v1.2-GGUF
Suggested Prompt Format
Llama 3 Instruct Format
Example:
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are [character]. You have a personality of [personality description]. [Describe scenario]<|eot_id|><|start_header_id|>user<|end_header_id|>
{{ user_message_1 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
{{ model_answer_1 }}<|eot_id|><|start_header_id|>user<|end_header_id|>
{{ user_message_2 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
- Downloads last month
- 82
3-bit
4-bit
5-bit