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Model: expertai/LLaMAntino-3-SLIMER-IT
Source: Original Platform

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README.md

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+---
+language:
+- it
+pipeline_tag: text-generation
+license: llama3
+tags:
+- facebook
+- meta
+- pythorch
+- llama
+- llama-3
+- llamantino
+- zero-shot NER
+- NER
+base_model: swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA
+---
+
+# SLIMER-IT: Show Less Instruct More Entity Recognition - Italian language
+
+SLIMER-IT is an LLM specifically instructed for zero-shot NER on Italian language.
+
+Github repository: https://github.com/andrewzamai/SLIMER_IT
+
+Instructed on a reduced number of tags (PER, ORG, LOC), it is designed to tackle never-seen-before Named Entity tags by leveraging a prompt enriched with a DEFINITION and GUIDELINES for the NE to be extracted.
+
+Built with Meta Llama 3, based on the Italian instruction-tuned version swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA
+
+<!DOCTYPE html>
+<html>
+<head>
+    <title>Instruction Tuning Prompt</title>
+    <style>
+        .container {
+            border: none;
+            padding: 5px;
+            width: 300px;
+            margin: 0 auto;
+            font-family: Arial, sans-serif;
+            font-size: 8px;
+            border-radius: 10px; /* Rounded borders for container */
+            overflow: hidden; /* Ensure child elements respect container's rounded borders */
+        }
+        .header {
+            background-color: black;
+            color: white;
+            padding: 5px;
+            text-align: center;
+            font-weight: bold;
+            font-size: 14px;
+            border-top-left-radius: 10px; /* Rounded top-left corner */
+            border-top-right-radius: 10px; /* Rounded top-right corner */
+        }
+        .content {
+            padding: 5px;
+        }
+        .definition, .guidelines {
+            padding: 5px;
+            border-radius: 10px; /* Rounded borders for definition and guidelines */
+        }
+        .definition {
+            background-color: #ffa3f0;
+        }
+        .guidelines {
+            background-color: #93e2fa;
+        }
+        .footer {
+            background-color: black;
+            color: white;
+            padding: 10px;
+            font-weight: bold;
+            border-bottom-left-radius: 10px;
+            border-bottom-right-radius: 10px;
+        }
+    </style>
+</head>
+<body>
+    <div class="container">
+        <div class="header">Instruction Tuning Prompt</div>
+        <div class="content">
+            <p>Ti viene fornito un input di testo (delimitato da tre virgolette) e un'istruzione.<br>
+            Leggi il testo e rispondi all'istruzione alla fine.</p>
+            <p>"""<br>
+            {input di testo}<br>
+            """</p>
+            <p><b>Istruzione:</b> Estrai tutte le entità di tipo <b>ENTITÀ MITOLOGICA</b> dal testo che hai letto.</p>
+            <p>Ti vengono fornite una <b>DEFINIZIONE</b> e alcune <b>LINEE GUIDA</b>.</p>
+            <div class="definition">
+                <p><b>DEFINIZIONE:</b> <b>ENTITÀ MITOLOGICA</b> denota personaggi, divinità, creature o figure mitologiche provenienti da tradizioni religiose, miti, leggende o folklore.</p>
+            </div>
+            <div class="guidelines">
+                <p><b>LINEE GUIDA:</b> Assicurati di non etichettare come ENTITÀ MITOLOGICA personaggi storici o letterari reali. Ad esempio, 'Alessandro Magno' è un personaggio storico, non una figura mitologica. Inoltre, fai attenzione a distinguere nomi comuni o nomi di luoghi che possono riferirsi anche a figure mitologiche, come 'Diana', che può essere un nome proprio e il nome della dea romana della caccia.</p>
+            </div>
+            <p>Restituisci una lista JSON di istanze di questo tipo. Restituisci una lista vuota se non sono presenti istanze.</p>
+        </div>
+        <div class="footer"></div>
+    </div>
+</body>
+</html>
+
+
+<!DOCTYPE html>
+<html lang="en">
+<head>
+<meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<title>JSON Template</title>
+<style>
+  body {
+    font-family: Arial, sans-serif;
+    line-height: 1.6;
+    padding: 20px;
+  }
+  .description {
+    font-weight: bold;
+    color: #333;
+    margin-bottom: 10px;
+  }
+  .template {
+    background-color: #f0f0f0;
+    padding: 10px;
+    border-radius: 5px;
+    margin-bottom: 20px;
+  }
+  .highlight-orange {
+    color: orange;
+    font-weight: bold;
+  }
+</style>
+</head>
+<body>
+  <div class="description">JSON SLIMER-IT prompt</div>
+  <div class="template">
+    <pre>{
+  "description": "SLIMER prompt for Italian",
+  "prompt_input": "<|start_header_id|>system<|end_header_id|>\n\n Sei un utile assistente.<|eot_id|>\n<|start_header_id|>user<|end_header_id|>\n\nTi viene fornito un input di testo (delimitato da tre virgolette) e un'istruzione. \nLeggi il testo e rispondi all'istruzione alla fine.\n\"\"\"\n{<span class="highlight-orange">input</span>}\n\"\"\"\nIstruzione: Estrai tutte le entità di tipo {<span class="highlight-orange">NE_name</span>} dal testo che hai letto. Ti vengono fornite una DEFINIZIONE e alcune LINEE GUIDA.\nDEFINIZIONE: {<span class="highlight-orange">definition</span>}\nLINEE GUIDA: {<span class="highlight-orange">guidelines</span>}\nRestituisci una lista JSON di istanze di questo tipo. Restituisci una lista vuota se non sono presenti istanze.<|eot_id|>\n<|start_header_id|>assistant<|end_header_id|>\n\n"
+  }</pre>
+  </div>
+</body>
+</html>
+
+
+```python
+from vllm import LLM, SamplingParams
+
+vllm_model = LLM(model="expertai/SLIMER-IT")
+
+sampling_params = SamplingParams(temperature=0, max_tokens=128)
+
+prompts = [prompter.generate_prompt(instruction, input) for instruction, input in instruction_input_pairs]
+responses = vllm_model.generate(prompts, sampling_params)
+```
+
+## Citation
+
+If you find SLIMER-IT useful in your research or work, please cite the following paper:
+
+``` latex
+@misc{zamai2024slimeritzeroshotneritalian,
+      title={SLIMER-IT: Zero-Shot NER on Italian Language}, 
+      author={Andrew Zamai and Leonardo Rigutini and Marco Maggini and Andrea Zugarini},
+      year={2024},
+      eprint={2409.15933},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2409.15933}, 
+}
+```

config.json

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+{
+  "_name_or_path": "swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": 128001,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 8192,
+  "max_seq_len": 2048,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.37.0",
+  "use_cache": true,
+  "vocab_size": 128256
+}

generation_config.json

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+{
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": [
+    128001,
+    128009
+  ],
+  "max_length": 8192,
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.37.0"
+}

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special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|eot_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "!"
+}

training_configs.yml

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+# fine-tuning Llama-3-8b instruct version on PileNER with topNE NEs and N samples per NE (N positive + N negative)
+
+#base_model: meta-llama/Meta-Llama-3-8B-Instruct
+#base_model: meta-llama/Meta-Llama-3-8B
+base_model: swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA
+prompt_template_name: llama3_italian
+# using dataset converted from MSEQA format to GenQA format (instruction, input, output) columns
+data_path: None
+val_data_path: None
+select_train_portion: -1
+val_set_size: -1  # if -1 use all validation data
+output_dir: None
+early_stopping_patience: 5
+#training hyperparams
+batch_size: 32
+micro_batch_size: 1
+num_epochs: 10
+learning_rate: 3.0e-4
+cutoff_len: 768
+
+warmup_steps: 60
+eval_steps: 20
+logging_steps: 5
+max_grad_norm: 1.0
+#lora hyperparams
+use_lora: True
+lora_alpha: 16
+lora_dropout: 0.05
+lora_r: 8
+lora_target_modules:
+- q_proj
+- v_proj
+- k_proj
+- v_proj
+#llm hyperparams
+# NTP loss only on Response
+train_on_inputs: False
+group_by_length: True
+#quant params
+load_8bit: False
+load_4bit: False
+#general param
+save_total_limit: 2
+use_flash_attention: False
+shuffle: True
+gradient_checkpointing: False