---
language:
- en
pipeline_tag: text-generation
tags:
- shining-valiant
- shining-valiant-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
- science
- physics
- biology
- chemistry
- compsci
- computer-science
- engineering
- technical
- conversational
- chat
- instruct
- llama-cpp
- gguf-my-repo
base_model: ValiantLabs/Llama3.1-8B-ShiningValiant2
datasets:
- sequelbox/Celestia
- sequelbox/Spurline
- sequelbox/Supernova
model_type: llama
license: llama3.1
model-index:
- name: Llama3.1-8B-ShiningValiant2
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Winogrande (5-Shot)
      type: Winogrande
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 75.85
      name: acc
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU College Biology (5-Shot)
      type: MMLU
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 68.75
      name: acc
    - type: acc
      value: 73.23
      name: acc
    - type: acc
      value: 46.0
      name: acc
    - type: acc
      value: 44.33
      name: acc
    - type: acc
      value: 53.19
      name: acc
    - type: acc
      value: 37.25
      name: acc
    - type: acc
      value: 42.38
      name: acc
    - type: acc
      value: 56.0
      name: acc
    - type: acc
      value: 63.0
      name: acc
    - type: acc
      value: 63.16
      name: acc
  - 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: 65.24
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-ShiningValiant2
      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: 26.35
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-ShiningValiant2
      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: 11.63
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-ShiningValiant2
      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: 8.95
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-ShiningValiant2
      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: 7.19
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-ShiningValiant2
      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: 26.38
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=ValiantLabs/Llama3.1-8B-ShiningValiant2
      name: Open LLM Leaderboard
---

# Triangle104/Llama3.1-8B-ShiningValiant2-Q4_K_S-GGUF
This model was converted to GGUF format from [`ValiantLabs/Llama3.1-8B-ShiningValiant2`](https://huggingface.co/ValiantLabs/Llama3.1-8B-ShiningValiant2) 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/ValiantLabs/Llama3.1-8B-ShiningValiant2) 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 Triangle104/Llama3.1-8B-ShiningValiant2-Q4_K_S-GGUF --hf-file llama3.1-8b-shiningvaliant2-q4_k_s.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Llama3.1-8B-ShiningValiant2-Q4_K_S-GGUF --hf-file llama3.1-8b-shiningvaliant2-q4_k_s.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/Llama3.1-8B-ShiningValiant2-Q4_K_S-GGUF --hf-file llama3.1-8b-shiningvaliant2-q4_k_s.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Llama3.1-8B-ShiningValiant2-Q4_K_S-GGUF --hf-file llama3.1-8b-shiningvaliant2-q4_k_s.gguf -c 2048
```