Triangle104/Llama3.1-8B-SEP-Chat-Q4_K_M-GGUF

This model was converted to GGUF format from ruggsea/Llama3.1-8B-SEP-Chat using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

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Model details:

This model is a LoRA finetune of meta-llama/Meta-Llama-3.1-8B trained on multi-turn philosophical conversations. It is designed to engage in philosophical discussions in a conversational yet rigorous manner, maintaining academic standards while being accessible. Model description

The model was trained using the TRL (Transformer Reinforcement Learning) library's chat template, enabling it to handle multi-turn conversations in a natural way. It builds upon the capabilities of its predecessor Llama3-stanford-encyclopedia-philosophy-QA but extends it to handle more interactive, back-and-forth philosophical discussions. Chat Format

The model uses the standard chat format with roles:

<|system|> {{system_prompt}} <|user|> {{user_message}} <|assistant|> {{assistant_response}}

Training Details

The model was trained with the following system prompt:

You are an expert and informative yet accessible Philosophy university professor. Students will engage with you in philosophical discussions. Respond to their questions and comments in a correct and rigorous but accessible way, maintaining academic standards while fostering understanding.

Training hyperparameters

The following hyperparameters were used during training:

Learning rate: 2e-5
Train batch size: 1
Gradient accumulation steps: 4
Effective batch size: 4
Optimizer: paged_adamw_8bit
LR scheduler: cosine with warmup
Warmup ratio: 0.03
Training epochs: 5
LoRA config:
    r: 256
    alpha: 128
    Target modules: all-linear
    Dropout: 0.05

Framework versions

PEFT 0.10.0
Transformers 4.40.1
PyTorch 2.2.2+cu121
TRL latest
Datasets 2.19.0
Tokenizers 0.19.1

Intended Use

This model is designed for:

Multi-turn philosophical discussions
Academic philosophical inquiry
Teaching and learning philosophy
Exploring philosophical concepts through dialogue

Limitations

The model should not be used as a substitute for professional philosophical advice or formal philosophical education
While the model aims to be accurate, its responses should be verified against authoritative sources
The model may occasionally generate plausible-sounding but incorrect philosophical arguments
As with all language models, it may exhibit biases present in its training data

License

This model is subject to the Meta Llama 2 license agreement. Please refer to Meta's licensing terms for usage requirements and restrictions. How to use

Here's an example of how to use the model:

from transformers import AutoModelForCausalLM, AutoTokenizer

Load model and tokenizer

model = AutoModelForCausalLM.from_pretrained("ruggsea/Llama3.1-SEP-Chat") tokenizer = AutoTokenizer.from_pretrained("ruggsea/Llama3.1-SEP-Chat")

Example conversation

messages = [ {"role": "user", "content": "What is the difference between ethics and morality?"} ]

Format prompt using chat template

prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=False )

Generate response

inputs = tokenizer(prompt, return_tensors="pt").to(model.device) outputs = model.generate(**inputs, max_new_tokens=512) response = tokenizer.decode(outputs[0], skip_special_tokens=True)

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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/Llama3.1-8B-SEP-Chat-Q4_K_M-GGUF --hf-file llama3.1-8b-sep-chat-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Llama3.1-8B-SEP-Chat-Q4_K_M-GGUF --hf-file llama3.1-8b-sep-chat-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/Llama3.1-8B-SEP-Chat-Q4_K_M-GGUF --hf-file llama3.1-8b-sep-chat-q4_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Llama3.1-8B-SEP-Chat-Q4_K_M-GGUF --hf-file llama3.1-8b-sep-chat-q4_k_m.gguf -c 2048