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---
language:
- en
license: llama3
library_name: transformers
base_model: arcee-ai/Llama-3.1-SuperNova-Lite
datasets:
- arcee-ai/EvolKit-20k
tags:
- llama-cpp
- gguf-my-repo
model-index:
- name: Llama-3.1-SuperNova-Lite
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 80.17
name: strict accuracy
source:
url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=arcee-ai/Llama-3.1-SuperNova-Lite
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 31.57
name: normalized accuracy
source:
url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=arcee-ai/Llama-3.1-SuperNova-Lite
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 15.48
name: exact match
source:
url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=arcee-ai/Llama-3.1-SuperNova-Lite
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 7.49
name: acc_norm
source:
url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=arcee-ai/Llama-3.1-SuperNova-Lite
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 11.67
name: acc_norm
source:
url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=arcee-ai/Llama-3.1-SuperNova-Lite
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 31.97
name: accuracy
source:
url: https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=arcee-ai/Llama-3.1-SuperNova-Lite
name: Open LLM Leaderboard
---
# Triangle104/Llama-3.1-SuperNova-Lite-Q8_0-GGUF
This model was converted to GGUF format from [`arcee-ai/Llama-3.1-SuperNova-Lite`](https://huggingface.co./arcee-ai/Llama-3.1-SuperNova-Lite) 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./arcee-ai/Llama-3.1-SuperNova-Lite) for more details on the model.
---
Model details:
-
Overview
Llama-3.1-SuperNova-Lite is an 8B parameter model developed by Arcee.ai, based on the Llama-3.1-8B-Instruct architecture. It is a distilled version of the larger Llama-3.1-405B-Instruct model, leveraging offline logits extracted from the 405B parameter variant. This 8B variation of Llama-3.1-SuperNova maintains high performance while offering exceptional instruction-following capabilities and domain-specific adaptability.
The model was trained using a state-of-the-art distillation pipeline and an instruction dataset generated with EvolKit, ensuring accuracy and efficiency across a wide range of tasks. For more information on its training, visit blog.arcee.ai.
Llama-3.1-SuperNova-Lite excels in both benchmark performance and real-world applications, providing the power of large-scale models in a more compact, efficient form ideal for organizations seeking high performance with reduced resource requirements.
---
## 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 Triangle104/Llama-3.1-SuperNova-Lite-Q8_0-GGUF --hf-file llama-3.1-supernova-lite-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Llama-3.1-SuperNova-Lite-Q8_0-GGUF --hf-file llama-3.1-supernova-lite-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 Triangle104/Llama-3.1-SuperNova-Lite-Q8_0-GGUF --hf-file llama-3.1-supernova-lite-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Llama-3.1-SuperNova-Lite-Q8_0-GGUF --hf-file llama-3.1-supernova-lite-q8_0.gguf -c 2048
```
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