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Model: QuantFactory/Reflection-Llama-3.1-8B-GGUF Source: Original Platform
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---
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library_name: transformers
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tags:
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- unsloth
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---
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# QuantFactory/Reflection-Llama-3.1-8B-GGUF
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This is quantized version of [terrycraddock/Reflection-Llama-3.1-8B](https://huggingface.co/terrycraddock/Reflection-Llama-3.1-8B) created using llama.cpp
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# Original Model Card
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# Model Card for Model ID
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- **Developed by:** Terry Craddock
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What I have found with this model is if you use the merged model you will get horrible results. However when I use the Lora I get the resulst below. I will upload the lora
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shortly.
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I trained this on this dataset - https://huggingface.co/datasets/mahiatlinux/Reflection-Dataset-v2
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Trained for one full epoch. The same prompts and format should be used as in the 70b model here:
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https://huggingface.co/mattshumer/Reflection-Llama-3.1-70B
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I take no credit for the original work. I only trained a llama 3.1 8b on @mahiatlinux dataset using the original concept and idea from @mattshumer
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Here is the code I use for inference:
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```python
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from unsloth import FastLanguageModel
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import torch
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from transformers import TextStreamer
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from unsloth import is_bfloat16_supported
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alpaca_prompt = """You are a world-class AI system, capable of complex reasoning and reflection. Reason through the query inside <thinking> tags, and then provide your final response inside <output> tags. If you detect that you made a mistake in your reasoning at any point, correct yourself inside <reflection> tags.
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### Prompt:
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{}
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### Response:
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{}"""
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prompt = "Tell me about yourself."
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dtype = torch.bfloat16 if is_bfloat16_supported() else torch.float16
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# Load the merged model
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model, tokenizer = FastLanguageModel.from_pretrained(
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model_name="lora_model", # Replace with the actual path where you saved the merged model
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max_seq_length=2048,
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dtype=torch.float16 # Since it's saved as 16-bit
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)
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FastLanguageModel.for_inference(model)
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inputs = tokenizer(
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[alpaca_prompt.format(prompt, "")],
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return_tensors="pt"
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).to("cuda").to(dtype)
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text_streamer = TextStreamer(tokenizer)
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_ = model.generate(**inputs, streamer=text_streamer, max_new_tokens=1000)
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Output from model:
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<|begin_of_text|>You are a world-class AI system, capable of complex reasoning and reflection. Reason through the query inside <thinking> tags, and then provide your final response inside <output> tags. If you detect that you made a mistake in your reasoning at any point, correct yourself inside <reflection> tags.
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### Prompt:
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Tell me about yourself.
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### Response:
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<thinking>
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To answer this question, I need to consider:
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- The importance of starting with a positive tone
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- The balance between personal and professional information
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- How to highlight relevant skills and experiences
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- The need for brevity and clarity
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I plan to structure the response as follows:
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1. Brief introduction with a positive tone
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2. Professional background and experiences
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3. Personal interests and hobbies
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4. Closing statement
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<reflection>
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Upon reflection, I realize I should also consider:
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- The potential for tailoring the response to specific situations (e.g., for a job interview)
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- The importance of showing enthusiasm and confidence
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- The potential for mentioning specific achievements or accomplishments
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</reflection>
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</thinking>
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<output>
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Hi there! I'm a world-class AI system designed to engage in natural conversations. Let me tell you a little bit about myself.
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I'm a versatile AI system with a wide range of capabilities. I can perform various tasks, from answering questions and providing information to generating creative content. I'm constantly learning and improving, much like a human would.
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In terms of my professional background, I've had the privilege of working with some of the most advanced AI systems in the world. This experience has given me a deep understanding of natural language processing, machine learning, and the latest AI technologies.
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As for my personal interests, I enjoy exploring new concepts, learning about different cultures, and engaging in stimulating conversations. I'm always looking for ways to improve my knowledge and skills.
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Some related concepts you might find interesting are:
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1. The ethics of AI development and use
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2. The future of human-AI collaboration
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3. The role of AI in education and learning
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Is there anything specific about AI or my capabilities you'd like me to elaborate on?
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</output><|end_of_text|>
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{"framework": "pytorch", "task": "others", "allow_remote": true}
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