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I'm not sure of why you would want to use raw llama-2 though when there is a million super strong instruction fine-tuned versions of llama-2 on HF hub that woul

by make3 3y ago

I'm not sure of why you would want to use raw llama-2 though when there is a million super strong instruction fine-tuned versions of llama-2 on HF hub that would do the job a million times better? Like Stability-AI's Beluga-2. See https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderb... About your second point, the goal is that the model can only generate JSON (for example), which can 100% be done by constraining which output token can and cannot be used.

nabakin 3y ago
Don't rely too much on automated benchmarks for LLMs. They are often gamed, made to overfit, and result in worse performance in the general case. Human evaluation is the gold standard and the Llama 2 paper gave significant evidence that Llama 2 70b chat is on-par, if not, better than ChatGPT for that metric so I tend to stick to it unless there is good reason not to.
- huevosabio 3y ago
  The problem with Llama 2 chat versions is that they have been RLHF-ed to death. You can't ask questions without getting a sermon of how your question may be inappropriate for this or that reason. I think it's worse on the smaller models, but still present in the 70B one.
  - dceddia 3y ago
    Apologies if you’d already seen this and were only trying to make a point, but you might like this article from a week or 2 ago that talks about how to run Llama 2 “uncensored” locally, and it seems to do a decent job of mitigating the sermons! Article: https://ollama.ai/blog/run-llama2-uncensored-locally https://ollama.ai/blog/run-llama2-uncensored-locally Discussion: https://news.ycombinator.com/item?id=36973584 https://news.ycombinator.com/item?id=36973584
    - superkuh 3y ago
      When you encounter "uncensored" in a llama model (1 or 2) what that means in that context is that the fine-tuning datasets used have had all refusals to respond removed. There's no way to uncensor the pre-trained model itself and fine-tuning only changes the style of the output.
  - nabakin 3y ago
    For sure, that's a good reason for using the uncensored fine-tuned versions. There are other good reasons too like expanded context size, codegen, and story writing/rp. Just be careful of extraordinary benchmarks. Btw, have you tried changing the default Llama 2 chat prompt? Meta tried to fine-tune it so that if you remove the safety part from the prompt, safety won't be applied[1]. Not sure how well it works myself, but worth a shot I guess [1] can be found in the Llama 2 paper
activatedgeek 3y ago
> I'm not sure of why you would want to use raw llama-2 Sure. My concern was not specific to llama-2, and was only using it as a placeholder example of a decent pre-trained base model. Replace it with your favorite base model, which you want to use for guided generation. My question is more fundamental - how does post-hoc guided generation interfere with the potential benefits of instruction-tuning? > About your second point, the goal is that the model can only generate JSON (for example), which can 100% be done by constraining which output token can and cannot be used. Mechanistically, yes. I am not arguing that. The whole point is to generate JSON that is "useful".

by make3 3y ago

nabakin 3y ago

Don't rely too much on automated benchmarks for LLMs. They are often gamed, made to overfit, and result in worse performance in the general case. Human evaluation is the gold standard and the Llama 2 paper gave significant evidence that Llama 2 70b chat is on-par, if not, better than ChatGPT for that metric so I tend to stick to it unless there is good reason not to.

huevosabio 3y ago
The problem with Llama 2 chat versions is that they have been RLHF-ed to death. You can't ask questions without getting a sermon of how your question may be inappropriate for this or that reason. I think it's worse on the smaller models, but still present in the 70B one.
- dceddia 3y ago
  Apologies if you’d already seen this and were only trying to make a point, but you might like this article from a week or 2 ago that talks about how to run Llama 2 “uncensored” locally, and it seems to do a decent job of mitigating the sermons! Article: https://ollama.ai/blog/run-llama2-uncensored-locally https://ollama.ai/blog/run-llama2-uncensored-locally Discussion: https://news.ycombinator.com/item?id=36973584 https://news.ycombinator.com/item?id=36973584
  - superkuh 3y ago
    When you encounter "uncensored" in a llama model (1 or 2) what that means in that context is that the fine-tuning datasets used have had all refusals to respond removed. There's no way to uncensor the pre-trained model itself and fine-tuning only changes the style of the output.
- nabakin 3y ago
  For sure, that's a good reason for using the uncensored fine-tuned versions. There are other good reasons too like expanded context size, codegen, and story writing/rp. Just be careful of extraordinary benchmarks. Btw, have you tried changing the default Llama 2 chat prompt? Meta tried to fine-tune it so that if you remove the safety part from the prompt, safety won't be applied[1]. Not sure how well it works myself, but worth a shot I guess [1] can be found in the Llama 2 paper

activatedgeek 3y ago

> I'm not sure of why you would want to use raw llama-2 Sure. My concern was not specific to llama-2, and was only using it as a placeholder example of a decent pre-trained base model. Replace it with your favorite base model, which you want to use for guided generation. My question is more fundamental - how does post-hoc guided generation interfere with the potential benefits of instruction-tuning? > About your second point, the goal is that the model can only generate JSON (for example), which can 100% be done by constraining which output token can and cannot be used. Mechanistically, yes. I am not arguing that. The whole point is to generate JSON that is "useful".