metadata
base_model: KOCDIGITAL/Kocdigital-LLM-8b-v0.1
language:
- tr
license: llama3
tags:
- llama-cpp
- gguf-my-repo
model-index:
- name: Kocdigital-LLM-8b-v0.1
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge TR
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc
value: 44.03
name: accuracy
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag TR
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc
value: 46.73
name: accuracy
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU TR
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 49.11
name: accuracy
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA TR
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: acc
value: 48.21
name: accuracy
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande TR
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 10
metrics:
- type: acc
value: 54.98
name: accuracy
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k TR
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 51.78
name: accuracy
serkandyck/Kocdigital-LLM-8b-v0.1-Q4_K_M-GGUF
This model was converted to GGUF format from KOCDIGITAL/Kocdigital-LLM-8b-v0.1
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 serkandyck/Kocdigital-LLM-8b-v0.1-Q4_K_M-GGUF --hf-file kocdigital-llm-8b-v0.1-q4_k_m.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo serkandyck/Kocdigital-LLM-8b-v0.1-Q4_K_M-GGUF --hf-file kocdigital-llm-8b-v0.1-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 serkandyck/Kocdigital-LLM-8b-v0.1-Q4_K_M-GGUF --hf-file kocdigital-llm-8b-v0.1-q4_k_m.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo serkandyck/Kocdigital-LLM-8b-v0.1-Q4_K_M-GGUF --hf-file kocdigital-llm-8b-v0.1-q4_k_m.gguf -c 2048