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RichardErkhov/
DeepMount00_-_Llama-3.1-8b-Ita-gguf

Name: DeepMount00_-_Llama-3.1-8b-Ita-gguf
Availability: InStock

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Llama-3.1-8b-ITA - GGUF

Model creator: https://huggingface.co/DeepMount00/
Original model: https://huggingface.co/DeepMount00/Llama-3.1-8b-ITA/

Name	Quant method	Size
Llama-3.1-8b-ITA.Q2_K.gguf	Q2_K	2.96GB
Llama-3.1-8b-ITA.Q3_K_S.gguf	Q3_K_S	3.41GB
Llama-3.1-8b-ITA.Q3_K.gguf	Q3_K	3.74GB
Llama-3.1-8b-ITA.Q3_K_M.gguf	Q3_K_M	3.74GB
Llama-3.1-8b-ITA.Q3_K_L.gguf	Q3_K_L	4.03GB
Llama-3.1-8b-ITA.IQ4_XS.gguf	IQ4_XS	4.18GB
Llama-3.1-8b-ITA.Q4_0.gguf	Q4_0	4.34GB
Llama-3.1-8b-ITA.IQ4_NL.gguf	IQ4_NL	4.38GB
Llama-3.1-8b-ITA.Q4_K_S.gguf	Q4_K_S	4.37GB
Llama-3.1-8b-ITA.Q4_K.gguf	Q4_K	4.58GB
Llama-3.1-8b-ITA.Q4_K_M.gguf	Q4_K_M	4.58GB
Llama-3.1-8b-ITA.Q4_1.gguf	Q4_1	4.78GB
Llama-3.1-8b-ITA.Q5_0.gguf	Q5_0	5.21GB
Llama-3.1-8b-ITA.Q5_K_S.gguf	Q5_K_S	5.21GB
Llama-3.1-8b-ITA.Q5_K.gguf	Q5_K	5.34GB
Llama-3.1-8b-ITA.Q5_K_M.gguf	Q5_K_M	5.34GB
Llama-3.1-8b-ITA.Q5_1.gguf	Q5_1	5.65GB
Llama-3.1-8b-ITA.Q6_K.gguf	Q6_K	6.14GB
Llama-3.1-8b-ITA.Q8_0.gguf	Q8_0	7.95GB

Original model description:

language:

en
it library_name: transformers base_model: meta-llama/Meta-Llama-3.1-8B-Instruct model-index:
name: Llama-3.1-8b-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: 79.17 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepMount00/Llama-3.1-8b-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: 30.93 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepMount00/Llama-3.1-8b-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: 10.88 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepMount00/Llama-3.1-8b-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: 5.03 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepMount00/Llama-3.1-8b-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: 11.4 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepMount00/Llama-3.1-8b-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: 31.96 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=DeepMount00/Llama-3.1-8b-ITA name: Open LLM Leaderboard

Model Architecture

Base Model: Meta-Llama-3.1-8B-Instruct
Specialization: Italian Language

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-3.1-8b-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

Detailed results can be found here

Metric	Value
Avg.	28.23
IFEval (0-Shot)	79.17
BBH (3-Shot)	30.93
MATH Lvl 5 (4-Shot)	10.88
GPQA (0-shot)	5.03
MuSR (0-shot)	11.40
MMLU-PRO (5-shot)	31.96

RichardErkhov/DeepMount00_-_Llama-3.1-8b-Ita-gguf