From e049347b376e0fab64c26385abbcafdc753ef7d1 Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Thu, 18 Jun 2026 00:46:12 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E7=94=B1ModelHub=20XC=E7=A4=BE=E5=8C=BA=E6=8F=90?= =?UTF-8?q?=E4=BE=9B=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Model: QuantFactory/Reflection-Llama-3.1-8B-GGUF Source: Original Platform --- .gitattributes | 49 ++++++++++++ README.md | 115 ++++++++++++++++++++++++++++ Reflection-Llama-3.1-8B.Q2_K.gguf | 3 + Reflection-Llama-3.1-8B.Q3_K_L.gguf | 3 + Reflection-Llama-3.1-8B.Q3_K_M.gguf | 3 + Reflection-Llama-3.1-8B.Q3_K_S.gguf | 3 + Reflection-Llama-3.1-8B.Q4_0.gguf | 3 + Reflection-Llama-3.1-8B.Q4_1.gguf | 3 + Reflection-Llama-3.1-8B.Q4_K_M.gguf | 3 + Reflection-Llama-3.1-8B.Q4_K_S.gguf | 3 + Reflection-Llama-3.1-8B.Q5_0.gguf | 3 + Reflection-Llama-3.1-8B.Q5_1.gguf | 3 + 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+![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ) + +# QuantFactory/Reflection-Llama-3.1-8B-GGUF +This is quantized version of [terrycraddock/Reflection-Llama-3.1-8B](https://huggingface.co/terrycraddock/Reflection-Llama-3.1-8B) created using llama.cpp + +# Original Model Card + + +# Model Card for Model ID + +- **Developed by:** Terry Craddock + +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 +shortly. + +I trained this on this dataset - https://huggingface.co/datasets/mahiatlinux/Reflection-Dataset-v2 + +Trained for one full epoch. The same prompts and format should be used as in the 70b model here: + +https://huggingface.co/mattshumer/Reflection-Llama-3.1-70B + +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 + +Here is the code I use for inference: + +```python +from unsloth import FastLanguageModel +import torch +from transformers import TextStreamer +from unsloth import is_bfloat16_supported + + +alpaca_prompt = """You are a world-class AI system, capable of complex reasoning and reflection. Reason through the query inside tags, and then provide your final response inside tags. If you detect that you made a mistake in your reasoning at any point, correct yourself inside tags. +### Prompt: +{} + +### Response: +{}""" + +prompt = "Tell me about yourself." + +dtype = torch.bfloat16 if is_bfloat16_supported() else torch.float16 + +# Load the merged model +model, tokenizer = FastLanguageModel.from_pretrained( + model_name="lora_model", # Replace with the actual path where you saved the merged model + max_seq_length=2048, + dtype=torch.float16 # Since it's saved as 16-bit +) + +FastLanguageModel.for_inference(model) +inputs = tokenizer( + [alpaca_prompt.format(prompt, "")], + return_tensors="pt" +).to("cuda").to(dtype) + +text_streamer = TextStreamer(tokenizer) +_ = model.generate(**inputs, streamer=text_streamer, max_new_tokens=1000) + + + +Output from model: + +<|begin_of_text|>You are a world-class AI system, capable of complex reasoning and reflection. Reason through the query inside tags, and then provide your final response inside tags. If you detect that you made a mistake in your reasoning at any point, correct yourself inside tags. +### Prompt: +Tell me about yourself. + +### Response: + +To answer this question, I need to consider: +- The importance of starting with a positive tone +- The balance between personal and professional information +- How to highlight relevant skills and experiences +- The need for brevity and clarity + +I plan to structure the response as follows: +1. Brief introduction with a positive tone +2. Professional background and experiences +3. Personal interests and hobbies +4. Closing statement + + +Upon reflection, I realize I should also consider: +- The potential for tailoring the response to specific situations (e.g., for a job interview) +- The importance of showing enthusiasm and confidence +- The potential for mentioning specific achievements or accomplishments + + + + +Hi there! I'm a world-class AI system designed to engage in natural conversations. Let me tell you a little bit about myself. + +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. + +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. + +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. + +Some related concepts you might find interesting are: +1. The ethics of AI development and use +2. The future of human-AI collaboration +3. 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