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Model: expertai/LLaMAntino-3-SLIMER-IT Source: Original Platform
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README.md
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---
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language:
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- it
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pipeline_tag: text-generation
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license: llama3
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tags:
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- facebook
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- meta
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- pythorch
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- llama
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- llama-3
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- llamantino
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- zero-shot NER
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- NER
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base_model: swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA
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---
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# SLIMER-IT: Show Less Instruct More Entity Recognition - Italian language
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SLIMER-IT is an LLM specifically instructed for zero-shot NER on Italian language.
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Github repository: https://github.com/andrewzamai/SLIMER_IT
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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.
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Built with Meta Llama 3, based on the Italian instruction-tuned version swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA
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<!DOCTYPE html>
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<html>
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<head>
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<title>Instruction Tuning Prompt</title>
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<style>
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.container {
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border: none;
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padding: 5px;
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width: 300px;
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margin: 0 auto;
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font-family: Arial, sans-serif;
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font-size: 8px;
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border-radius: 10px; /* Rounded borders for container */
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overflow: hidden; /* Ensure child elements respect container's rounded borders */
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}
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.header {
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background-color: black;
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color: white;
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padding: 5px;
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text-align: center;
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font-weight: bold;
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font-size: 14px;
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border-top-left-radius: 10px; /* Rounded top-left corner */
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border-top-right-radius: 10px; /* Rounded top-right corner */
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}
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.content {
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padding: 5px;
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}
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.definition, .guidelines {
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padding: 5px;
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border-radius: 10px; /* Rounded borders for definition and guidelines */
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}
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.definition {
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background-color: #ffa3f0;
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}
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.guidelines {
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background-color: #93e2fa;
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}
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.footer {
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background-color: black;
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color: white;
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padding: 10px;
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font-weight: bold;
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border-bottom-left-radius: 10px;
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border-bottom-right-radius: 10px;
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}
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</style>
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</head>
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<body>
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<div class="container">
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<div class="header">Instruction Tuning Prompt</div>
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<div class="content">
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<p>Ti viene fornito un input di testo (delimitato da tre virgolette) e un'istruzione.<br>
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Leggi il testo e rispondi all'istruzione alla fine.</p>
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<p>"""<br>
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{input di testo}<br>
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"""</p>
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<p><b>Istruzione:</b> Estrai tutte le entità di tipo <b>ENTITÀ MITOLOGICA</b> dal testo che hai letto.</p>
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<p>Ti vengono fornite una <b>DEFINIZIONE</b> e alcune <b>LINEE GUIDA</b>.</p>
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<div class="definition">
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<p><b>DEFINIZIONE:</b> <b>ENTITÀ MITOLOGICA</b> denota personaggi, divinità, creature o figure mitologiche provenienti da tradizioni religiose, miti, leggende o folklore.</p>
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</div>
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<div class="guidelines">
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<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>
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</div>
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<p>Restituisci una lista JSON di istanze di questo tipo. Restituisci una lista vuota se non sono presenti istanze.</p>
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</div>
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<div class="footer"></div>
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</div>
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</body>
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</html>
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>JSON Template</title>
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<style>
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body {
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font-family: Arial, sans-serif;
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line-height: 1.6;
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padding: 20px;
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}
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.description {
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font-weight: bold;
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color: #333;
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margin-bottom: 10px;
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}
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.template {
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background-color: #f0f0f0;
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padding: 10px;
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border-radius: 5px;
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margin-bottom: 20px;
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.highlight-orange {
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color: orange;
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font-weight: bold;
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}
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</style>
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</head>
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<body>
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<div class="description">JSON SLIMER-IT prompt</div>
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<div class="template">
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<pre>{
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"description": "SLIMER prompt for Italian",
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"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"
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}</pre>
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</div>
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</body>
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</html>
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```python
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from vllm import LLM, SamplingParams
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vllm_model = LLM(model="expertai/SLIMER-IT")
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sampling_params = SamplingParams(temperature=0, max_tokens=128)
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prompts = [prompter.generate_prompt(instruction, input) for instruction, input in instruction_input_pairs]
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responses = vllm_model.generate(prompts, sampling_params)
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```
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## Citation
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If you find SLIMER-IT useful in your research or work, please cite the following paper:
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``` latex
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@misc{zamai2024slimeritzeroshotneritalian,
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title={SLIMER-IT: Zero-Shot NER on Italian Language},
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author={Andrew Zamai and Leonardo Rigutini and Marco Maggini and Andrea Zugarini},
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year={2024},
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eprint={2409.15933},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2409.15933},
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}
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```
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config.json
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"_name_or_path": "swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA",
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"eos_token_id": 128001,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 8192,
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"max_seq_len": 2048,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 500000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.37.0",
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"use_cache": true,
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"vocab_size": 128256
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}
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"model.layers.6.self_attn.q_proj.weight": "model-00001-of-00004.safetensors",
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"model.layers.7.self_attn.k_proj.weight": "model-00001-of-00004.safetensors",
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"model.layers.9.input_layernorm.weight": "model-00002-of-00004.safetensors",
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"model.layers.9.self_attn.o_proj.weight": "model-00002-of-00004.safetensors",
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"model.layers.9.self_attn.v_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.norm.weight": "model-00004-of-00004.safetensors"
|
||||
}
|
||||
}
|
||||
17
special_tokens_map.json
Normal file
17
special_tokens_map.json
Normal file
@@ -0,0 +1,17 @@
|
||||
{
|
||||
"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": "!"
|
||||
}
|
||||
410503
tokenizer.json
Normal file
410503
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
2064
tokenizer_config.json
Normal file
2064
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
46
training_configs.yml
Normal file
46
training_configs.yml
Normal file
@@ -0,0 +1,46 @@
|
||||
# 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
|
||||
Reference in New Issue
Block a user