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Model: duyntnet/Phi-3.5-mini-instruct-imatrix-GGUF Source: Original Platform
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---
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license: other
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language:
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- en
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pipeline_tag: text-generation
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inference: false
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tags:
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- transformers
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- gguf
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- imatrix
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- Phi-3.5-mini-instruct
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---
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Quantizations of https://huggingface.co/microsoft/Phi-3.5-mini-instruct
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### Inference Clients/UIs
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* [llama.cpp](https://github.com/ggerganov/llama.cpp)
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* [JanAI](https://github.com/janhq/jan)
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* [KoboldCPP](https://github.com/LostRuins/koboldcpp)
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* [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
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* [ollama](https://github.com/ollama/ollama)
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* [GPT4All](https://github.com/nomic-ai/gpt4all)
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---
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# From original readme
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Phi-3.5-mini is a lightweight, state-of-the-art open model built upon datasets used for Phi-3 - synthetic data and filtered publicly available websites - with a focus on very high-quality, reasoning dense data. The model belongs to the Phi-3 model family and supports 128K token context length. The model underwent a rigorous enhancement process, incorporating both supervised fine-tuning, proximal policy optimization, and direct preference optimization to ensure precise instruction adherence and robust safety measures.
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## Usage
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### Requirements
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Phi-3 family has been integrated in the `4.43.0` version of `transformers`. The current `transformers` version can be verified with: `pip list | grep transformers`.
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Examples of required packages:
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```
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flash_attn==2.5.8
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torch==2.3.1
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accelerate==0.31.0
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transformers==4.43.0
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```
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Phi-3.5-mini-instruct is also available in [Azure AI Studio](https://aka.ms/try-phi3.5mini)
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### Tokenizer
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Phi-3.5-mini-Instruct supports a vocabulary size of up to `32064` tokens. The [tokenizer files](https://huggingface.co/microsoft/Phi-3.5-mini-instruct/blob/main/added_tokens.json) already provide placeholder tokens that can be used for downstream fine-tuning, but they can also be extended up to the model's vocabulary size.
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### Input Formats
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Given the nature of the training data, the Phi-3.5-mini-instruct model is best suited for prompts using the chat format as follows:
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```
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<|system|>
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You are a helpful assistant.<|end|>
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<|user|>
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How to explain Internet for a medieval knight?<|end|>
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<|assistant|>
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```
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### Loading the model locally
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After obtaining the Phi-3.5-mini-instruct model checkpoint, users can use this sample code for inference.
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
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torch.random.manual_seed(0)
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model = AutoModelForCausalLM.from_pretrained(
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"microsoft/Phi-3.5-mini-instruct",
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device_map="cuda",
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torch_dtype="auto",
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trust_remote_code=True,
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)
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tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3.5-mini-instruct")
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messages = [
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{"role": "system", "content": "You are a helpful AI assistant."},
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{"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"},
|
||||
{"role": "assistant", "content": "Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey."},
|
||||
{"role": "user", "content": "What about solving an 2x + 3 = 7 equation?"},
|
||||
]
|
||||
|
||||
pipe = pipeline(
|
||||
"text-generation",
|
||||
model=model,
|
||||
tokenizer=tokenizer,
|
||||
)
|
||||
|
||||
generation_args = {
|
||||
"max_new_tokens": 500,
|
||||
"return_full_text": False,
|
||||
"temperature": 0.0,
|
||||
"do_sample": False,
|
||||
}
|
||||
|
||||
output = pipe(messages, **generation_args)
|
||||
print(output[0]['generated_text'])
|
||||
```
|
||||
Reference in New Issue
Block a user