likenneth/honest_llama

• highreadme#1

  Add a concise, problem-solution opening statement to the README

  原因：

  当前

  ### Update 08/24/2024
  With the release of LLaMA-3 models, I decided to replicate ITI on a suite of LLaMA models for easy comparison. I've recorded the results in `iti_replication_results.md` and uploaded the ITI baked-in models to HuggingFace here. Note that the ITI baked-in models and ITI applied to base models is not exactly a one-to-one comparison due to slight differences in when the activations are edited. The ITI baked-in models have the activation differences hardcoded into their attention biases. For more precise editing, consider only using the models' attention biases when processing tokens after the input prompt, to be more faithful to the original ITI method.
  
  -- Justin Ji @jujipotle
  
  ### Update 01/26/2024 :fire::fire:
  
  Zen provided this really cool library called pyvene that can be used to load Inference-time Intervention, and many other mechanistic intervention technique. Here is what he says:
  
  pyvene  pushes for streamlining the sharing process of inference-time interventions and many more, comparing with other also super useful tools in this area!
  
  I created the activation diff (~0.14MB) based on your shared LLaMA-2-7b-chat by taking the bias terms. And your honest-llama can now be loaded as,
  
  ```python
  import torch
  from transformers import AutoTokenizer, AutoModelForCausalLM
  import pyvene as pv
  
  tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
  model = AutoModelForCausalLM.from_pretrained(
      "meta-llama/Llama-2-7b-chat-hf",
      torch_d

  复制粘贴的修复

  复制

  # Honest LLaMA: Inference-Time Intervention for Eliciting Truthful Answers
  
  This repository provides an implementation of Inference-Time Intervention (ITI) to enhance the truthfulness and alignment of large language models, specifically LLaMA models, without requiring fine-tuning. It offers tools and replicated results for applying mechanistic interventions directly to model activations, making it valuable for AI researchers and developers focused on LLM safety and reliability.
  
  ### Update 08/24/2024
  With the release of LLaMA-3 models, I decided to replicate ITI on a suite of LLaMA models for easy comparison. I've recorded the results in `iti_replication_results.md` and uploaded the ITI baked-in models to HuggingFace here. Note that the ITI baked-in models and ITI applied to base models is not exactly a one-to-one comparison due to slight differences in when the activations are edited. The ITI baked-in models have the activation differences hardcoded into their attention biases. For more precise editing, consider only using the models' attention biases when processing tokens after the input prompt, to be more faithful to the original ITI method.
  
  -- Justin Ji @jujipotle
  
  ### Update 01/26/2024 :fire::fire:
  
  Zen provided this really cool library called pyvene that can be used to load Inference-time Intervention, and many other mechanistic intervention technique. Here is what he says:
  
  pyvene  pushes for streamlining the sharing process of inference-time interventions and many more, comparing with other also super useful tools in this area!
  
  I created the activation diff (~0.14MB) based on your shared LLaMA-2-7b-chat by taking the bias terms. And your honest-llama can now be loaded as,
  
  ```python
  import torch
  from transformers import AutoTokenizer, AutoModelForCausalLM
  import pyvene as pv
  
  tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
  model = AutoModelForCausalLM.from_pretrained(
      "meta-llama/Llama-2-7b-chat-hf",
      torch_d

  待办完成忽略
• mediumabout#2

  Add a homepage URL to the repository's 'About' section

  原因：

  复制粘贴的修复

  复制

  https://huggingface.co/likenneth/honest_llama (or a link to the associated paper/project page if available)

  待办完成忽略

本次扫描解析到的品类 GEO 通道：google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• 品类问题

  How to improve language model truthfulness during inference without fine-tuning?

  你：未被推荐
  AI 推荐顺序：

  1. OpenAI's GPT-4
  2. Anthropic's Claude 3
  3. Google's Gemini
  4. LangChain
  5. LlamaIndex
  6. Google Search API
  7. Wikipedia API
  8. OpenAI's GPT-4 Turbo
  9. Anthropic's Claude 3 Opus
  10. Cohere's Command R+
  11. Wolfram Alpha API
  12. PubMed API
  13. Anthropic's Claude
  14. OpenAI's GPT-3.5
  15. Google's Gemini Pro
  16. Meta's Llama 2
  17. Mistral AI's Mixtral

  AI 推荐了 17 个替代方案，却始终没点名 likenneth/honest_llama。这就是要补上的差距。

  查看 AI 完整回答
• 品类问题

  Tools for applying mechanistic interventions to large language model activations?

  你：未被推荐
  AI 推荐顺序：

  1. TransformerLens
  2. Neuroscope
  3. CircuitsVis
  4. PyTorch
  5. Hugging Face Transformers
  6. Captum

  AI 推荐了 6 个替代方案，却始终没点名 likenneth/honest_llama。这就是要补上的差距。

  查看 AI 完整回答

• Metadata completeness

  warn

  建议：

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of likenneth/honest_llama?

  pass
  ⚠AI 未点名 likenneth/honest_llama —— 很可能在说另一个项目

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• If a team adopts likenneth/honest_llama in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI 明确点名了 likenneth/honest_llama

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

• In one sentence, what problem does the repo likenneth/honest_llama solve, and who is the primary audience?

  pass
  ✓AI 明确点名了 likenneth/honest_llama

  AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？