---
base_model: nbeerbower/Mistral-Small-Drummer-22B
datasets:
- jondurbin/gutenberg-dpo-v0.1
- nbeerbower/gutenberg2-dpo
library_name: transformers
license: other
license_name: mrl
license_link: https://mistral.ai/licenses/MRL-0.1.md
tags:
- llama-cpp
- gguf-my-repo
model-index:
- name: Mistral-Small-Drummer-22B
  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: 63.31
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/Mistral-Small-Drummer-22B
      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: 40.12
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/Mistral-Small-Drummer-22B
      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: 16.69
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/Mistral-Small-Drummer-22B
      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: 12.42
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/Mistral-Small-Drummer-22B
      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: 9.8
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/Mistral-Small-Drummer-22B
      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: 34.39
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/Mistral-Small-Drummer-22B
      name: Open LLM Leaderboard
---

# Triangle104/Mistral-Small-Drummer-22B-Q4_K_S-GGUF
This model was converted to GGUF format from [`nbeerbower/Mistral-Small-Drummer-22B`](https://huggingface.co/nbeerbower/Mistral-Small-Drummer-22B) 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/nbeerbower/Mistral-Small-Drummer-22B) for more details on the model.

---
Model details:
-
mistralai/Mistral-Small-Instruct-2409 finetuned on jondurbin/gutenberg-dpo-v0.1 and nbeerbower/gutenberg2-dpo.
Method

ORPO tuned with 2xA40 on RunPod for 1 epoch.

learning_rate=4e-6,
lr_scheduler_type="linear",
beta=0.1,
per_device_train_batch_size=4,
per_device_eval_batch_size=4,
gradient_accumulation_steps=8,
optim="paged_adamw_8bit",
num_train_epochs=1,

Dataset was prepared using Mistral-Small Instruct format.

Fine-tune Llama 3 with ORPO
Open LLM Leaderboard Evaluation Results

Detailed results can be found here
Metric 	Value
Avg. 	29.45
IFEval (0-Shot) 	63.31
BBH (3-Shot) 	40.12
MATH Lvl 5 (4-Shot) 	16.69
GPQA (0-shot) 	12.42
MuSR (0-shot) 	9.80
MMLU-PRO (5-shot) 	34.39

---
## 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/Mistral-Small-Drummer-22B-Q4_K_S-GGUF --hf-file mistral-small-drummer-22b-q4_k_s.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Mistral-Small-Drummer-22B-Q4_K_S-GGUF --hf-file mistral-small-drummer-22b-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/Mistral-Small-Drummer-22B-Q4_K_S-GGUF --hf-file mistral-small-drummer-22b-q4_k_s.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Mistral-Small-Drummer-22B-Q4_K_S-GGUF --hf-file mistral-small-drummer-22b-q4_k_s.gguf -c 2048
```