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Model: sag-uniroma2/extremITA-Camoscio-7b
Source: Original Platform

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README.md

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+---
+inference: false
+license: openrail
+language:
+- it
+datasets:
+- teelinsan/camoscio
+---
+
+# ExtremITA Camoscio 7 bilion parameters
+This is the base model trained on Italian instructions, a sibling of Alpaca.   
+It is based on [tellinsan/camoscio-7b-llama](https://huggingface.co/teelinsan/camoscio-7b-llama) adapters and the original LLaMA model, and it adds nothing new to [tellinsan/camoscio-7b-llama](https://huggingface.co/teelinsan/camoscio-7b-llama). Our version is the merged model with the adapters in order to obtain a more stable model that can be further fine-tuned, which we did for the [EVALITA 2023](https://www.evalita.it/campaigns/evalita-2023/) challenge.
+
+# Usage
+Checkout the github repository for more insights and codes: https://github.com/crux82/ExtremITA
+
+```python
+from transformers import LLaMATokenizer, LLaMAForCausalLM, GenerationConfig
+import torch
+
+tokenizer = LLaMATokenizer.from_pretrained("yahma/llama-7b-hf")
+model = LLaMAForCausalLM.from_pretrained(
+    "sag-uniroma2/extremITA-Camoscio-7b",
+    load_in_8bit=True,
+    device_map="auto",
+)
+
+generation_config = GenerationConfig(
+  temperature=0.2,
+  top_p=0.75,
+  top_k=40,
+  num_beams=4,
+)
+
+prompts = [
+  "Riassumi la storia di Pinocchio",
+  "Scrivi un programma che stampa i numeri da 1 a 100. Ma per i multipli \
+  di tre stampa 'Fizz' al posto del numero e per i multipli di cinque \
+  stampa 'Buzz'. Per i numeri che sono multipli sia di tre che di cinque \
+  stampa 'FizzBuzz'."
+]
+
+inputs = tokenizer(prompts, return_tensors="pt", padding=True, \
+                  truncation=True).to(model.device)
+
+with torch.no_grad():
+  gen_outputs = model.generate(
+    **inputs,
+    generation_config=generation_config,
+    return_dict_in_generate=True,
+    output_scores=True,
+  )
+
+  for i in range(len(gen_outputs[0])):
+    output = tokenizer.decode(gen_outputs[0][i], skip_special_tokens=True)
+    print(output)
+```
+
+# Citation
+```
+@inproceedings{hromei2023extremita,
+  author       = {Claudiu Daniel Hromei and
+                  Danilo Croce and
+                  Valerio Basile and
+                  Roberto Basili},
+  title        = {ExtremITA at EVALITA 2023: Multi-Task Sustainable Scaling to Large Language Models at its Extreme},
+  booktitle    = {Proceedings of the Eighth Evaluation Campaign of Natural Language
+                  Processing and Speech Tools for Italian. Final Workshop (EVALITA 2023)},
+  publisher    = {CEUR.org},
+  year         = {2023},
+  month        = {September},
+  address      = {Parma, Italy}
+}
+```

config.json

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+{
+  "_name_or_path": "decapoda-research/llama-7b-hf",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "bos_token_id": 0,
+  "eos_token_id": 1,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "max_position_embeddings": 2048,
+  "max_sequence_length": 2048,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "pad_token_id": -1,
+  "rms_norm_eps": 1e-06,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.29.0.dev0",
+  "use_cache": true,
+  "vocab_size": 32000
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "eos_token_id": 1,
+  "pad_token_id": 0,
+  "transformers_version": "4.29.0.dev0"
+}

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special_tokens_map.json

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+}

tokenizer_config.json

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