README.md

---
language:
- en
- ko
library_name: transformers
license: cc-by-nc-4.0
pipeline_tag: text-generation
model_id: kakaocorp/kanana-nano-2.1b-base
repo: kakaocorp/kanana-nano-2.1b-base
developers: Kanana LLM
training_regime: bf16 mixed precision
tags:
- llama-cpp
- gguf-my-repo
base_model: kakaocorp/kanana-nano-2.1b-base
model-index:
- name: kanana-nano-2.1b-base
  results:
  - task:
      type: multiple_choice
      name: mmlu
    dataset:
      name: mmlu (5-shots)
      type: hails/mmlu_no_train
    metrics:
    - type: acc
      value: 54.83
      name: acc
  - task:
      type: generate_until
      name: kmmlu
    dataset:
      name: kmmlu-direct (5-shots)
      type: HAERAE-HUB/KMMLU
    metrics:
    - type: exact_match
      value: 44.83
      name: exact_match
  - task:
      type: multiple_choice
      name: haerae
    dataset:
      name: haerae (5-shots)
      type: HAERAE-HUB/HAE_RAE_BENCH
    metrics:
    - type: acc_norm
      value: 77.09
      name: acc_norm
  - task:
      type: generate_until
      name: gsm8k
    dataset:
      name: gsm8k (5-shots)
      type: openai/gsm8k
    metrics:
    - type: exact_match
      value: 46.32
      name: exact_match_strict
  - task:
      type: generate_until
      name: humaneval
    dataset:
      name: humaneval (0-shots)
      type: openai/openai_humaneval
    metrics:
    - type: pass@1
      value: 31.1
      name: pass@1
  - task:
      type: generate_until
      name: mbpp
    dataset:
      name: mbpp (3-shots)
      type: google-research-datasets/mbpp
    metrics:
    - type: pass@1
      value: 46.2
      name: pass@1
---

# nonemonehpark/kanana-nano-2.1b-base-Q8_0-GGUF
This model was converted to GGUF format from [`kakaocorp/kanana-nano-2.1b-base`](https://huggingface.co/kakaocorp/kanana-nano-2.1b-base) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/kakaocorp/kanana-nano-2.1b-base) for more details on the model.

## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo nonemonehpark/kanana-nano-2.1b-base-Q8_0-GGUF --hf-file kanana-nano-2.1b-base-q8_0.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo nonemonehpark/kanana-nano-2.1b-base-Q8_0-GGUF --hf-file kanana-nano-2.1b-base-q8_0.gguf -c 2048
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) 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 nonemonehpark/kanana-nano-2.1b-base-Q8_0-GGUF --hf-file kanana-nano-2.1b-base-q8_0.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo nonemonehpark/kanana-nano-2.1b-base-Q8_0-GGUF --hf-file kanana-nano-2.1b-base-q8_0.gguf -c 2048
```