README.md

---
language:
- en
license: llama3.2
library_name: transformers
base_model: Nexesenex/meditsolutions_Llama-3.2-SUN-1B-Instruct
datasets:
- argilla/OpenHermesPreferences
- argilla/magpie-ultra-v0.1
- argilla/Capybara-Preferences-Filtered
- mlabonne/open-perfectblend
- HuggingFaceTB/everyday-conversations-llama3.1-2k
- WizardLMTeam/WizardLM_evol_instruct_V2_196k
- ProlificAI/social-reasoning-rlhf
- allenai/tulu-3-sft-mixture
- allenai/llama-3.1-tulu-3-8b-preference-mixture
pipeline_tag: text-generation
tags:
- llama-cpp
- gguf-my-repo
model-index:
- name: Llama-3.2-SUN-1B-Instruct
  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: 64.13
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=meditsolutions/Llama-3.2-SUN-1B-Instruct
      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: 9.18
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=meditsolutions/Llama-3.2-SUN-1B-Instruct
      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: 4.61
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=meditsolutions/Llama-3.2-SUN-1B-Instruct
      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: 0.0
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=meditsolutions/Llama-3.2-SUN-1B-Instruct
      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: 4.05
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=meditsolutions/Llama-3.2-SUN-1B-Instruct
      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: 8.68
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=meditsolutions/Llama-3.2-SUN-1B-Instruct
      name: Open LLM Leaderboard
---

# surya-ravindra/meditsolutions_Llama-3.2-SUN-1B-Instruct-Q4_K_M-GGUF
This model was converted to GGUF format from [`Nexesenex/meditsolutions_Llama-3.2-SUN-1B-Instruct`](https://huggingface.co/Nexesenex/meditsolutions_Llama-3.2-SUN-1B-Instruct) 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/Nexesenex/meditsolutions_Llama-3.2-SUN-1B-Instruct) 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 surya-ravindra/meditsolutions_Llama-3.2-SUN-1B-Instruct-Q4_K_M-GGUF --hf-file meditsolutions_llama-3.2-sun-1b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo surya-ravindra/meditsolutions_Llama-3.2-SUN-1B-Instruct-Q4_K_M-GGUF --hf-file meditsolutions_llama-3.2-sun-1b-instruct-q4_k_m.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 surya-ravindra/meditsolutions_Llama-3.2-SUN-1B-Instruct-Q4_K_M-GGUF --hf-file meditsolutions_llama-3.2-sun-1b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo surya-ravindra/meditsolutions_Llama-3.2-SUN-1B-Instruct-Q4_K_M-GGUF --hf-file meditsolutions_llama-3.2-sun-1b-instruct-q4_k_m.gguf -c 2048
```