felipe-carlos-ipms/phi-3-portuguese-tom-cat-4k-instruct-Q4_K_M-GGUF
This model was converted to GGUF format from rhaymison/phi-3-portuguese-tom-cat-4k-instruct
using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the original model card for more details on the model.
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 felipe-carlos-ipms/phi-3-portuguese-tom-cat-4k-instruct-Q4_K_M-GGUF --hf-file phi-3-portuguese-tom-cat-4k-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo felipe-carlos-ipms/phi-3-portuguese-tom-cat-4k-instruct-Q4_K_M-GGUF --hf-file phi-3-portuguese-tom-cat-4k-instruct-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 felipe-carlos-ipms/phi-3-portuguese-tom-cat-4k-instruct-Q4_K_M-GGUF --hf-file phi-3-portuguese-tom-cat-4k-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo felipe-carlos-ipms/phi-3-portuguese-tom-cat-4k-instruct-Q4_K_M-GGUF --hf-file phi-3-portuguese-tom-cat-4k-instruct-q4_k_m.gguf -c 2048
- Downloads last month
- 16
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Model tree for felipe-carlos-ipms/phi-3-portuguese-tom-cat-4k-instruct-Q4_K_M-GGUF
Base model
microsoft/Phi-3-mini-4k-instructDataset used to train felipe-carlos-ipms/phi-3-portuguese-tom-cat-4k-instruct-Q4_K_M-GGUF
Evaluation results
- accuracy on ENEM Challenge (No Images)Open Portuguese LLM Leaderboard61.580
- accuracy on BLUEX (No Images)Open Portuguese LLM Leaderboard50.630
- accuracy on OAB ExamsOpen Portuguese LLM Leaderboard43.690
- f1-macro on Assin2 RTEtest set Open Portuguese LLM Leaderboard91.540
- pearson on Assin2 STStest set Open Portuguese LLM Leaderboard75.270
- f1-macro on FaQuAD NLItest set Open Portuguese LLM Leaderboard47.460
- f1-macro on HateBR Binarytest set Open Portuguese LLM Leaderboard83.010
- f1-macro on PT Hate Speech Binarytest set Open Portuguese LLM Leaderboard70.190
- f1-macro on tweetSentBRtest set Open Portuguese LLM Leaderboard57.780