初始化项目，由ModelHub XC社区提供模型
Model: MoxoffSrL/Volare Source: Original Platform
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README.md
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---
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license: mit
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language:
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- it
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- en
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library_name: transformers
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tags:
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- sft
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- it
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- gemma
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- chatml
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---
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# Model Information
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Volare is an updated version of [Gemma7B](https://huggingface.co/google/gemma-7b), specifically fine-tuned with SFT and LoRA adjustments.
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- It's trained on publicly available datasets, like [SQUAD-it](https://huggingface.co/datasets/squad_it), and datasets we've created in-house.
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- it's designed to understand and maintain context, making it ideal for Retrieval Augmented Generation (RAG) tasks and applications requiring contextual awareness.
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# Evaluation
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We evaluated the model using the same test sets as used for the [Open Ita LLM Leaderboard](https://huggingface.co/spaces/FinancialSupport/open_ita_llm_leaderboard)
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| hellaswag_it acc_norm | arc_it acc_norm | m_mmlu_it 5-shot acc | Average | F1 |
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|:----------------------| :--------------- | :-------------------- | :------- | :-- |
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| 0.6474 | 0.4671 | 0.5521 | 0.555 | 69.82 |
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## Usage
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Be sure to install these dependencies before running the program
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```python
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!pip install transformers torch sentencepiece
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```
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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device = "cpu" # if you want to use the gpu make sure to have cuda toolkit installed and change this to "cuda"
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model = AutoModelForCausalLM.from_pretrained("MoxoffSpA/Volare")
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tokenizer = AutoTokenizer.from_pretrained("MoxoffSpA/Volare")
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question = """Quanto è alta la torre di Pisa?"""
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context = """
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La Torre di Pisa è un campanile del XII secolo, famoso per la sua inclinazione. Alta circa 56 metri.
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"""
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prompt = f"Domanda: {question}, contesto: {context}"
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messages = [
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{"role": "user", "content": prompt}
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]
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encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
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model_inputs = encodeds.to(device)
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model.to(device)
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generated_ids = model.generate(
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model_inputs, # The input to the model
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max_new_tokens=128, # Limiting the maximum number of new tokens generated
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do_sample=True, # Enabling sampling to introduce randomness in the generation
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temperature=0.1, # Setting temperature to control the randomness, lower values make it more deterministic
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top_p=0.95, # Using nucleus sampling with top-p filtering for more coherent generation
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eos_token_id=tokenizer.eos_token_id # Specifying the token that indicates the end of a sequence
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)
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decoded_output = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
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trimmed_output = decoded_output.strip()
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print(trimmed_output)
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```
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## Bias, Risks and Limitations
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Volare has not been aligned to human preferences for safety within the RLHF phase or deployed with in-the-loop filtering of
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responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition
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of the corpus was used to train the base model, however it is likely to have included a mix of Web data and technical sources
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like books and code.
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## Links to resources
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- SQUAD-it dataset: https://huggingface.co/datasets/squad_it
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- Gemma-7b model: https://huggingface.co/google/gemma-7b
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- Open Ita LLM Leaderbord: https://huggingface.co/spaces/FinancialSupport/open_ita_llm_leaderboard
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## Quantized versions
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We have published as well the 4 bit and 8 bit versions of this model:
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https://huggingface.co/MoxoffSpA/VolareQuantized
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## The Moxoff Team
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Jacopo Abate, Marco D'Ambra, Luigi Simeone, Gianpaolo Francesco Trotta
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config.json
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{
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"_name_or_path": "google/gemma-7b",
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"architectures": [
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"GemmaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"hidden_size": 3072,
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"initializer_range": 0.02,
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"intermediate_size": 24576,
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"max_position_embeddings": 8192,
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"model_type": "gemma",
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"num_attention_heads": 16,
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"num_hidden_layers": 28,
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"use_cache": true,
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"vocab_size": 256000
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}
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|
||||
"tokenizer_class": "GemmaTokenizer",
|
||||
"unk_token": "<unk>",
|
||||
"use_default_system_prompt": false
|
||||
}
|
||||
Reference in New Issue
Block a user