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Model: DeepMount00/Llama-3-8b-Ita
Source: Original Platform

README.md

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+---
+language:
+- it
+- en
+license: llama3
+library_name: transformers
+base_model: meta-llama/Meta-Llama-3-8B
+datasets:
+- DeepMount00/llm_ita_ultra
+model-index:
+- name: Llama-3-8b-Ita
+  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: 75.3
+      name: strict accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepMount00/Llama-3-8b-Ita
+      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: 28.08
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepMount00/Llama-3-8b-Ita
+      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: 5.36
+      name: exact match
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepMount00/Llama-3-8b-Ita
+      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: 7.38
+      name: acc_norm
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepMount00/Llama-3-8b-Ita
+      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: 11.68
+      name: acc_norm
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepMount00/Llama-3-8b-Ita
+      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: 31.69
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepMount00/Llama-3-8b-Ita
+      name: Open LLM Leaderboard
+---
+
+
+---
+
+**💡 Found this resource helpful?** Creating and maintaining open source AI models and datasets requires significant computational resources. If this work has been valuable to you, consider [supporting my research](https://buymeacoffee.com/michele.montebovi) to help me continue building tools that benefit the entire AI community. Every contribution directly funds more open source innovation! ☕
+
+---
+
+## Model Architecture
+- **Base Model:** [Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B)
+- **Specialization:** Italian Language
+
+## Evaluation
+
+For a detailed comparison of model performance, check out the [Leaderboard for Italian Language Models](https://huggingface.co/spaces/FinancialSupport/open_ita_llm_leaderboard).
+
+Here's a breakdown of the performance metrics:
+
+| Metric                      | hellaswag_it acc_norm | arc_it acc_norm | m_mmlu_it 5-shot acc | Average |
+|:----------------------------|:----------------------|:----------------|:---------------------|:--------|
+| **Accuracy Normalized**     | 0.6518                | 0.5441        | 0.5729              | 0.5896  |
+
+---
+
+## How to Use
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+MODEL_NAME = "DeepMount00/Llama-3-8b-Ita"
+
+model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype=torch.bfloat16).eval()
+model.to(device)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+
+def generate_answer(prompt):
+    messages = [
+        {"role": "user", "content": prompt},
+    ]
+    model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(device)
+    generated_ids = model.generate(model_inputs, max_new_tokens=200, do_sample=True,
+                                          temperature=0.001)
+    decoded = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
+    return decoded[0]
+
+prompt = "Come si apre un file json in python?"
+answer = generate_answer(prompt)
+print(answer)
+```
+---
+## Developer
+[Michele Montebovi]
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_DeepMount00__Llama-3-8b-Ita)
+
+|      Metric       |Value|
+|-------------------|----:|
+|Avg.               |26.58|
+|IFEval (0-Shot)    |75.30|
+|BBH (3-Shot)       |28.08|
+|MATH Lvl 5 (4-Shot)| 5.36|
+|GPQA (0-shot)      | 7.38|
+|MuSR (0-shot)      |11.68|
+|MMLU-PRO (5-shot)  |31.69|
+