Triangle104/L3.1-Pneuma-8B-Q4_K_S-GGUF

This model was converted to GGUF format from Replete-AI/L3.1-Pneuma-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.

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Model details:

This model is a fine-tuned version of meta-llama/Llama-3.1-8B-Instruct on the Sandevistan dataset. It achieves the following results on the evaluation set:

Loss: 2.4357

This model is designed to challenge common paradigms in training Large Language Models, giving them a focus on user experience over profitability. These are highly experimental, and need preference training in order to increase their effectiveness. It seems to have retained a large amount of the biases that we were trying to eliminate from the corporate instruct models.

Intended uses & limitations

Chatting, conversation, and assistance in small downstream tasks.

Large Language Models work incredibly differently from humans, so while we are capable of training and rewarding them to act just like us in many ways, you should treat it as a simulation and use the Socratic method when engaging with them. You, as an end-user should always remain in control of your own thoughts and decisions, and use AI as a way to improve yourself rather than becoming dependent on it.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 7.8e-06
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 16
total_train_batch_size: 128
optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
num_epochs: 2

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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/L3.1-Pneuma-8B-Q4_K_S-GGUF --hf-file l3.1-pneuma-8b-q4_k_s.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/L3.1-Pneuma-8B-Q4_K_S-GGUF --hf-file l3.1-pneuma-8b-q4_k_s.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/L3.1-Pneuma-8B-Q4_K_S-GGUF --hf-file l3.1-pneuma-8b-q4_k_s.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/L3.1-Pneuma-8B-Q4_K_S-GGUF --hf-file l3.1-pneuma-8b-q4_k_s.gguf -c 2048