Text Generation
GGUF
English
fireplace
fireplace-2
valiant
valiant-labs
llama
llama-3.1
llama-3.1-instruct
llama-3.1-instruct-8b
llama-3
llama-3-instruct
llama-3-instruct-8b
8b
function-calling
sql
database
data-visualization
matplotlib
json
conversational
chat
instruct
llama-cpp
gguf-my-repo
Eval Results
Inference Endpoints
adrlau/Llama3.1-8B-Fireplace2-Q4_K_M-GGUF
This model was converted to GGUF format from ValiantLabs/Llama3.1-8B-Fireplace2
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 adrlau/Llama3.1-8B-Fireplace2-Q4_K_M-GGUF --hf-file llama3.1-8b-fireplace2-q4_k_m.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo adrlau/Llama3.1-8B-Fireplace2-Q4_K_M-GGUF --hf-file llama3.1-8b-fireplace2-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 adrlau/Llama3.1-8B-Fireplace2-Q4_K_M-GGUF --hf-file llama3.1-8b-fireplace2-q4_k_m.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo adrlau/Llama3.1-8B-Fireplace2-Q4_K_M-GGUF --hf-file llama3.1-8b-fireplace2-q4_k_m.gguf -c 2048
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Model tree for adrlau/Llama3.1-8B-Fireplace2-Q4_K_M-GGUF
Base model
meta-llama/Llama-3.1-8B
Finetuned
meta-llama/Llama-3.1-8B-Instruct
Finetuned
ValiantLabs/Llama3.1-8B-Fireplace2
Evaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard54.830
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard24.070
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard5.820
- acc_norm on GPQA (0-shot)Open LLM Leaderboard5.150
- acc_norm on MuSR (0-shot)Open LLM Leaderboard4.380
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard15.630