Triangle104/Dumpling-Mistral-Nemo-8B-Q6_K-GGUF

This model was converted to GGUF format from nbeerbower/Dumpling-Mistral-Nemo-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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🧪 Experimental

An attempt to recover intelligence with a quick train, results are meh

Dumpling-Mistral-Nemo-8B

nbeerbower/mistral-nemo-kartoffel-PRUNE3 finetuned on:

-nbeerbower/GreatFirewall-DPO

-nbeerbower/Schule-DPO

-nbeerbower/Purpura-DPO

-nbeerbower/Arkhaios-DPO

-jondurbin/truthy-dpo-v0.1

-antiven0m/physical-reasoning-dpo

-flammenai/Date-DPO-NoAsterisks

-flammenai/Prude-Phi3-DPO

-Atsunori/HelpSteer2-DPO (1,000 samples)

-jondurbin/gutenberg-dpo-v0.1

-nbeerbower/gutenberg2-dpo

-nbeerbower/gutenberg-moderne-dpo.

Method

QLoRA ORPO tune with 2x RTX 3090 for 2 epochs.

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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/Dumpling-Mistral-Nemo-8B-Q6_K-GGUF --hf-file dumpling-mistral-nemo-8b-q6_k.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Dumpling-Mistral-Nemo-8B-Q6_K-GGUF --hf-file dumpling-mistral-nemo-8b-q6_k.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/Dumpling-Mistral-Nemo-8B-Q6_K-GGUF --hf-file dumpling-mistral-nemo-8b-q6_k.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Dumpling-Mistral-Nemo-8B-Q6_K-GGUF --hf-file dumpling-mistral-nemo-8b-q6_k.gguf -c 2048