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README.md

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+---
+base_model: OpenLLM-Ro/RoLlama3.1-8b-Instruct-2025-04-23
+datasets:
+- OpenLLM-Ro/ro_sft_alpaca
+- OpenLLM-Ro/ro_sft_alpaca_gpt4
+- OpenLLM-Ro/ro_sft_dolly
+- OpenLLM-Ro/ro_sft_selfinstruct_gpt4
+- OpenLLM-Ro/ro_sft_norobots
+- OpenLLM-Ro/ro_sft_orca
+- OpenLLM-Ro/ro_sft_camel
+- OpenLLM-Ro/ro_sft_oasst
+- OpenLLM-Ro/ro_sft_ultrachat
+- OpenLLM-Ro/ro_sft_magpie_mt
+- OpenLLM-Ro/ro_sft_magpie_reasoning
+language:
+- ro
+license: cc-by-nc-4.0
+tags:
+- llama-cpp
+- gguf-my-repo
+model-index:
+- name: OpenLLM-Ro/RoLlama3.1-8b-Instruct-2025-04-23
+  results:
+  - task:
+      type: text-generation
+    dataset:
+      name: RoMT-Bench
+      type: RoMT-Bench
+    metrics:
+    - type: Score
+      value: 6.43
+      name: Score
+    - type: Score
+      value: 6.78
+      name: First turn
+    - type: Score
+      value: 6.09
+      name: Second turn
+  - task:
+      type: text-generation
+    dataset:
+      name: RoCulturaBench
+      type: RoCulturaBench
+    metrics:
+    - type: Score
+      value: 4.28
+      name: Score
+  - task:
+      type: text-generation
+    dataset:
+      name: Romanian_Academic_Benchmarks
+      type: Romanian_Academic_Benchmarks
+    metrics:
+    - type: accuracy
+      value: 53.36
+      name: Average accuracy
+  - task:
+      type: text-generation
+    dataset:
+      name: OpenLLM-Ro/ro_arc_challenge
+      type: OpenLLM-Ro/ro_arc_challenge
+    metrics:
+    - type: accuracy
+      value: 48.97
+      name: Average accuracy
+    - type: accuracy
+      value: 45.24
+      name: 0-shot
+    - type: accuracy
+      value: 47.67
+      name: 1-shot
+    - type: accuracy
+      value: 49.36
+      name: 3-shot
+    - type: accuracy
+      value: 50.13
+      name: 5-shot
+    - type: accuracy
+      value: 50.81
+      name: 10-shot
+    - type: accuracy
+      value: 50.64
+      name: 25-shot
+  - task:
+      type: text-generation
+    dataset:
+      name: OpenLLM-Ro/ro_mmlu
+      type: OpenLLM-Ro/ro_mmlu
+    metrics:
+    - type: accuracy
+      value: 55.17
+      name: Average accuracy
+    - type: accuracy
+      value: 54.23
+      name: 0-shot
+    - type: accuracy
+      value: 56.36
+      name: 1-shot
+    - type: accuracy
+      value: 55.34
+      name: 3-shot
+    - type: accuracy
+      value: 54.74
+      name: 5-shot
+  - task:
+      type: text-generation
+    dataset:
+      name: OpenLLM-Ro/ro_winogrande
+      type: OpenLLM-Ro/ro_winogrande
+    metrics:
+    - type: accuracy
+      value: 66.52
+      name: Average accuracy
+    - type: accuracy
+      value: 64.96
+      name: 0-shot
+    - type: accuracy
+      value: 66.77
+      name: 1-shot
+    - type: accuracy
+      value: 67.09
+      name: 3-shot
+    - type: accuracy
+      value: 67.25
+      name: 5-shot
+  - task:
+      type: text-generation
+    dataset:
+      name: OpenLLM-Ro/ro_hellaswag
+      type: OpenLLM-Ro/ro_hellaswag
+    metrics:
+    - type: accuracy
+      value: 60.73
+      name: Average accuracy
+    - type: accuracy
+      value: 59.72
+      name: 0-shot
+    - type: accuracy
+      value: 60.3
+      name: 1-shot
+    - type: accuracy
+      value: 60.87
+      name: 3-shot
+    - type: accuracy
+      value: 61.14
+      name: 5-shot
+    - type: accuracy
+      value: 61.63
+      name: 10-shot
+  - task:
+      type: text-generation
+    dataset:
+      name: OpenLLM-Ro/ro_gsm8k
+      type: OpenLLM-Ro/ro_gsm8k
+    metrics:
+    - type: accuracy
+      value: 42.03
+      name: Average accuracy
+    - type: accuracy
+      value: 30.86
+      name: 1-shot
+    - type: accuracy
+      value: 43.9
+      name: 3-shot
+    - type: accuracy
+      value: 51.33
+      name: 5-shot
+  - task:
+      type: text-generation
+    dataset:
+      name: OpenLLM-Ro/ro_truthfulqa
+      type: OpenLLM-Ro/ro_truthfulqa
+    metrics:
+    - type: accuracy
+      value: 46.71
+      name: Average accuracy
+  - task:
+      type: text-generation
+    dataset:
+      name: LaRoSeDa_binary
+      type: LaRoSeDa_binary
+    metrics:
+    - type: macro-f1
+      value: 95.32
+      name: Average macro-f1
+    - type: macro-f1
+      value: 90.97
+      name: 0-shot
+    - type: macro-f1
+      value: 95.53
+      name: 1-shot
+    - type: macro-f1
+      value: 97.1
+      name: 3-shot
+    - type: macro-f1
+      value: 97.67
+      name: 5-shot
+  - task:
+      type: text-generation
+    dataset:
+      name: LaRoSeDa_multiclass
+      type: LaRoSeDa_multiclass
+    metrics:
+    - type: macro-f1
+      value: 60.84
+      name: Average macro-f1
+    - type: macro-f1
+      value: 63.2
+      name: 0-shot
+    - type: macro-f1
+      value: 64.47
+      name: 1-shot
+    - type: macro-f1
+      value: 55.88
+      name: 3-shot
+    - type: macro-f1
+      value: 59.8
+      name: 5-shot
+  - task:
+      type: text-generation
+    dataset:
+      name: WMT_EN-RO
+      type: WMT_EN-RO
+    metrics:
+    - type: bleu
+      value: 23.18
+      name: Average bleu
+    - type: bleu
+      value: 4.92
+      name: 0-shot
+    - type: bleu
+      value: 28.01
+      name: 1-shot
+    - type: bleu
+      value: 30.16
+      name: 3-shot
+    - type: bleu
+      value: 29.61
+      name: 5-shot
+  - task:
+      type: text-generation
+    dataset:
+      name: WMT_RO-EN
+      type: WMT_RO-EN
+    metrics:
+    - type: bleu
+      value: 25.11
+      name: Average bleu
+    - type: bleu
+      value: 1.43
+      name: 0-shot
+    - type: bleu
+      value: 24.78
+      name: 1-shot
+    - type: bleu
+      value: 37.31
+      name: 3-shot
+    - type: bleu
+      value: 36.93
+      name: 5-shot
+  - task:
+      type: text-generation
+    dataset:
+      name: XQuAD
+      type: XQuAD
+    metrics:
+    - type: exact_match
+      value: 10.74
+      name: Average exact_match
+    - type: f1
+      value: 19.75
+      name: Average f1
+  - task:
+      type: text-generation
+    dataset:
+      name: STS
+      type: STS
+    metrics:
+    - type: spearman
+      value: 73.53
+      name: Average spearman
+    - type: pearson
+      value: 74.93
+      name: Average pearson
+  - task:
+      type: text-generation
+    dataset:
+      name: XQuAD_EM
+      type: XQuAD_EM
+    metrics:
+    - type: exact_match
+      value: 11.18
+      name: 0-shot
+    - type: exact_match
+      value: 26.47
+      name: 1-shot
+    - type: exact_match
+      value: 3.95
+      name: 3-shot
+    - type: exact_match
+      value: 1.34
+      name: 5-shot
+  - task:
+      type: text-generation
+    dataset:
+      name: XQuAD_F1
+      type: XQuAD_F1
+    metrics:
+    - type: f1
+      value: 25.76
+      name: 0-shot
+    - type: f1
+      value: 39.25
+      name: 1-shot
+    - type: f1
+      value: 8.4
+      name: 3-shot
+    - type: f1
+      value: 5.58
+      name: 5-shot
+  - task:
+      type: text-generation
+    dataset:
+      name: STS_Spearman
+      type: STS_Spearman
+    metrics:
+    - type: spearman
+      value: 73.52
+      name: 1-shot
+    - type: spearman
+      value: 74.02
+      name: 3-shot
+    - type: spearman
+      value: 73.06
+      name: 5-shot
+  - task:
+      type: text-generation
+    dataset:
+      name: STS_Pearson
+      type: STS_Pearson
+    metrics:
+    - type: pearson
+      value: 75.81
+      name: 1-shot
+    - type: pearson
+      value: 74.54
+      name: 3-shot
+    - type: pearson
+      value: 74.43
+      name: 5-shot
+---
+
+# LinigDrake2875/RoLlama3.1-8b-Instruct-2025-04-23-Q4_K_M-GGUF
+This model was converted to GGUF format from [`OpenLLM-Ro/RoLlama3.1-8b-Instruct-2025-04-23`](https://huggingface.co/OpenLLM-Ro/RoLlama3.1-8b-Instruct-2025-04-23) 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/OpenLLM-Ro/RoLlama3.1-8b-Instruct-2025-04-23) 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 LinigDrake2875/RoLlama3.1-8b-Instruct-2025-04-23-Q4_K_M-GGUF --hf-file rollama3.1-8b-instruct-2025-04-23-q4_k_m.gguf -p "The meaning to life and the universe is"
+```
+
+### Server:
+```bash
+llama-server --hf-repo LinigDrake2875/RoLlama3.1-8b-Instruct-2025-04-23-Q4_K_M-GGUF --hf-file rollama3.1-8b-instruct-2025-04-23-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 LinigDrake2875/RoLlama3.1-8b-Instruct-2025-04-23-Q4_K_M-GGUF --hf-file rollama3.1-8b-instruct-2025-04-23-q4_k_m.gguf -p "The meaning to life and the universe is"
+```
+or 
+```
+./llama-server --hf-repo LinigDrake2875/RoLlama3.1-8b-Instruct-2025-04-23-Q4_K_M-GGUF --hf-file rollama3.1-8b-instruct-2025-04-23-q4_k_m.gguf -c 2048
+```