初始化项目，由ModelHub XC社区提供模型
Model: Ramikan-BR/Qwen2-0.5B-v15 Source: Original Platform
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ModelHub XC
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README.md
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README.md
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---
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base_model: unsloth/qwen2-0.5b-bnb-4bit
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language:
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- en
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license: apache-2.0
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- qwen2
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- trl
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- sft
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---
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# 1 - Question :
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alpaca_prompt = Copied from above
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FastLanguageModel.for_inference(model) # Enable native 2x faster inference
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inputs = tokenizer(
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[
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alpaca_prompt.format(
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"Continue the fibonnaci sequence.", # instruction
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"1, 1, 2, 3, 5, 8", # input
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"", # output - leave this blank for generation!
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)
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], return_tensors = "pt").to("cuda")
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outputs = model.generate(**inputs, max_new_tokens = 128, use_cache = True)
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tokenizer.batch_decode(outputs)
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# 1 - Answer :
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['Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Input:\nContinue the fibonnaci sequence.\n\n### Output:\n1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6765, 10946, 17711, 28657, 46368, 75025, 121393, 196418, 317811']
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# 2 - Question :
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alpaca_prompt = Copied from above
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FastLanguageModel.for_inference(model) # Enable native 2x faster inference
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inputs = tokenizer(
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[
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alpaca_prompt.format(
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"What is fibonacci sequence?", # instruction
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"", # input
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"", # output - leave this blank for generation!
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)
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], return_tensors = "pt").to("cuda")
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from transformers import TextStreamer
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text_streamer = TextStreamer(tokenizer)
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_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)
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# 2 - Answer :
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Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
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### Input:
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What is fibonacci sequence?
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### Output:
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The Fibonacci sequence is a series of numbers in which each number is the sum of the two preceding ones, usually starting with 0 and 1. The sequence goes like this: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6765, 1
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# 3 - Question :
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if False:
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from unsloth import FastLanguageModel
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model, tokenizer = FastLanguageModel.from_pretrained(
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model_name = "lora_model", # YOUR MODEL YOU USED FOR TRAINING
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max_seq_length = max_seq_length,
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dtype = dtype,
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load_in_4bit = load_in_4bit,
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)
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FastLanguageModel.for_inference(model) # Enable native 2x faster inference
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alpaca_prompt = You MUST copy from above!
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inputs = tokenizer(
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[
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alpaca_prompt.format(
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"I need train a AI offline on my computer, give me a code good for this case.", # instruction
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"", # input
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"", # output - leave this blank for generation!
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)
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], return_tensors = "pt").to("cuda")
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from transformers import TextStreamer
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text_streamer = TextStreamer(tokenizer)
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_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 4096)
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# 3 - Answer :
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Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
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### Input:
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I need train a AI offline on my computer, give me a code good for this case.
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### Output:
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Sure, here's a simple example of how you can train an AI model on a computer using Python and TensorFlow:
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```python
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import tensorflow as tf
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from tensorflow import keras
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from tensorflow.keras import layers
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# Define the model
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model = tf.keras.Sequential([
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layers.Dense(64, activation='relu', input_shape=(100,)),
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layers.Dense(64, activation='relu'),
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layers.Dense(1)
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])
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# Compile the model
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model.compile(optimizer='adam',
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loss='mean_squared_error',
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metrics=['mean_absolute_error'])
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# Train the model
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model.fit(X_train, y_train, epochs=100, batch_size=32)
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# Evaluate the model
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model.evaluate(X_test, y_test)
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```
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In this example, we are using the Keras library to create a sequential model. The model consists of two dense layers with ReLU activation. The first layer has 64 units and the second layer has 64 units. The output layer has 1 unit. The mean squared error is used as the loss function, and the mean absolute error is used as the metric for evaluation. The `adam` optimizer is used for training, and the `mean_squared_error` metric is used for evaluation.
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Please note that this is a very simple example and you may need to adjust the model architecture, number of layers, number of units, and other parameters depending on your specific use case.<|endoftext|>
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# Uploaded model
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- **Developed by:** Ramikan-BR
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- **License:** apache-2.0
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- **Finetuned from model :** unsloth/qwen2-0.5b-bnb-4bit
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This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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