How to use from
llama.cpp
Install from brew
brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf CerebrumTech/cere-gemma-2-9b-tr:
# Run inference directly in the terminal:
llama-cli -hf CerebrumTech/cere-gemma-2-9b-tr:
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf CerebrumTech/cere-gemma-2-9b-tr:
# Run inference directly in the terminal:
llama-cli -hf CerebrumTech/cere-gemma-2-9b-tr:
Use pre-built binary
# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf CerebrumTech/cere-gemma-2-9b-tr:
# Run inference directly in the terminal:
./llama-cli -hf CerebrumTech/cere-gemma-2-9b-tr:
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf CerebrumTech/cere-gemma-2-9b-tr:
# Run inference directly in the terminal:
./build/bin/llama-cli -hf CerebrumTech/cere-gemma-2-9b-tr:
Use Docker
docker model run hf.co/CerebrumTech/cere-gemma-2-9b-tr:
Quick Links

CEREBRUM LLM

Cere-llm-gemma-2-9b-it Model Card

Cere-llm-gemma-2-9b-it is a finetuned version of gemma-2-9b-it. It is trained on synthetically generated and natural preference datasets.

Model Details

Model Description

We fine-tuned google/gemma-2-9b-it

  • Developed by: Cerebrum Tech
  • Model type: Causal Language Model
  • License: gemma
  • Finetuned from model: google/gemma-2-9b-it

How to Get Started with the Model

import torch
from transformers import pipeline

model_id = "Cerebrum/cere-llm-gemma-2-ito"

generator = pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device="cuda",
)
outputs = generator([{"role": "user", "content": "Türkiye'nin başkenti neresidir?"}],
                      do_sample=False,
                      eos_token_id=[generator.tokenizer.convert_tokens_to_ids("<end_of_turn>"), generator.tokenizer.eos_token_id],
                      max_new_tokens=200)
print(outputs[0]['generated_text'])
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gemma2
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