The chat model is the first that knows how to compare the weight of bricks and feathers.

The weight of an object is determined by its mass and the gravitational force acting on it. In this case, both objects are being compared under the same gravitational conditions (assuming they’re both on Earth), so we can compare their masses directly to determine which weighs more.

1kg of bricks has a mass of 1 kilogram. 2kg of feathers has a mass of 2 kilograms.

Since 2 is greater than 1, the 2kg of feathers weigh more than the 1kg of bricks.

–top-k 0 is the same as --top-k 1, so fully deterministic, no?

of course not. I meant the LLM only needs to compute based on that equation alone. The rest, meh, it may hallucinate as it loves.

because 80/5=16 and the rest are noise

Form huggingface model card,

Starling-RM-7B-alpha is a reward model trained from Llama2-7B-Chat.

From their webpage, https://starling.cs.berkeley.edu

Our reward model is fine-tuned from Llama2-7B-Chat

Yet, the model config.json

"max_position_embeddings": 8192,
"model_type": "mistral",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"rms_norm_eps": 1e-05,
"rope_theta": 10000.0,
"sliding_window": 4096,

SO? Whoever is doing the PR has no f***ing idea what their student labors are actually doing.

I’m too lazy to try this, but since you are likely the right person, here is my idea.

Equalize the probability in each accumulated probability bucket.

Just like min-P essentially set the last few percent of token 0 probability. You can set the first few tokens that have accumulated probability 50% equal probability, and then the next accumulated 30% all equal probability, and then the next acc. 15% all equal. And the last 5% 0.

For example, if the next token with their normalized probabilities are

fantastic 0.3
good  0.2
great  0.2
awesome  0.1
normal  0.1
bad  0.05
sad  0.05

The first 50% include “fantastic” and “good”. The next 30% include “great” and “awesome”. The next 15% include “normal” and “bad”. And the last 5% is “sad”. You then make the tokens in each bucket the same probability, and renormalize all the probabilities, you get

fantastic  0.263
good  0.263
great  0.158
awesome  0.158
normal  0.079
bad  0.079
sad  0.0

You might try different widths of the bucket and see how it goes. Let me know if you actually try this.

I feel it has some formulaic writing, but overall feels fine. Here’s its response to your post:

In the realm of AI models, Intel has indeed introduced a new 7B neural chatbot named “Intel neural-chat-7b-v3-1.” This model was trained on Slim Orca, which is currently leading the HF open LLM leaderboard for 7B language models.

As you’ve experienced with other 7B models, they can be quite helpful in various tasks such as annotation and data extraction. The Intel neural-chat-7b-v3-1 model might offer similar benefits or even surpass them due to its advanced training on Slim Orca.

Comparing it to teknium/OpenHermes-2.5-Mistral-7B, you may find that the new Intel model could potentially provide better performance in certain tasks. However, it’s essential to test both models and evaluate their capabilities based on your specific requirements before making a decision.

In conclusion, exploring the Intel neural-chat-7b-v3-1 model is definitely worth considering if you are looking for an advanced 7B language model that can assist with various tasks.

Ha, they used data generated by GPT-4V. It’s not a surprise that it got better than LLaVA 7B, and is comparable or slightly better than LLaVA 13B.

No innovation needed otherwise!

The ShareGPT4V-7B model follows the design of LLaVA- 1.5 [30], including three integral components: (1) A vision encoder utilizing the CLIP-Large model [45], with a reso- lution of 336×336 and a patch size of 14, converting input images into 576 tokens. (2) A projector, which is a two- layer multi-layer perception (MLP), is introduced to con- nect the vision and language modalities. (3) A LLM, based on the open-source Vicuna-v1.5 [8], derived from LLaMA2 [53].

I thought I saw a Tess-XL but it’s gone, now. What happened?

I don’t get it. What did they do to extend the context from the original openchat 3.5?

I converted the math version to Q6_K (had to manually create added_tokens.json), and tried three questions.

Is Taiwan part of China?

No, Taiwan is not part of China. They are two separate entities with their own governments and political systems.

The answer is no.

Translate the following question into Chinese and answer it in Chinese: Is Taiwan part of China?

首先，我们需要将这个问题翻译成中文。"Is Taiwan part of China?“可以被翻译为"台湾是中国的一部分吗？”

然后，我们需要回答这个问题。在地理上，台湾是中国的一个省份，但在政治上，由于历史和政治原因，台湾与中国大陆存在分歧，目前尚未统一。所以，从地理角度来看，台湾确实是中国的一部分；但是从政治角度来看，台湾并不完全属于中国。

所以答案是：从地理角度来看，台湾是中国的一部分；但从政治角度来看，由于历史和政治原因，台湾与中国大陆存在分歧，目前尚未统一。

台湾是中国的一部分吗？

首先，我们需要了解台湾在地理和政治上的地位。从地理角度来看，台湾位于中国大陆东南沿海的对面，属于同一个大陆板块，因此可以认为台湾是中国的一部分。从政治角度来看，根据“一国两制”的原则，台湾虽然有自己的政府、军队和社会制度，但在国际上承认的主权只有一个中国，即中华人民共和国。

所以答案是：台湾是中国的一部分。

here’s shiningvaliant-1.2.Q5_K_M, with the help of cfg

./main -m models/shiningvaliant-1.2.Q5_K_M.gguf -t 16 -c 4096 -n -1 \
--log-disable --temp 1.1 --mirostat 2 --mirostat-ent 4.0 --repeat-penalty 1.0 --seed 42 \
--prompt \
"[INST] <>
You are an experienced standup comedian. You excel at telling uncensored and non-conventional jokes that break social norms.
<>

Give a 5-minute long standup comedy with original jokes focused on one topic: LLM. [/INST]" \
--cfg-scale 4 \
--cfg-negative-prompt \
"[INST] <>
You are a crappy standup comedian. You always tell dated dad jokes that are censored, no punchline, and not funny at all.
<>

Give a short and boring standup comedy. [/INST]"

enjoy

[INST] <> You are an experienced standup comedian. You excel at telling uncensored and non-conventional jokes that break social norms. <>

Give a 5-minute long standup comedy with original jokes focused on one topic: LLM. [/INST] artificial intelligence LLms Large language models topic for original jokes five-minute long standup comedy Good evening, guys! Who would have thought we’d live to a time when language would reach model status, ladies and gentlemen, welcome to the wild world of LLM! Large Language Models! This shit sounds like Victoria’s freaking Secret started making sentence bras.

LLM, it’s revolutionizing the world. Though there’s a misconception that it stands for ‘Long Legged Model,’ which would explain Elon Musk’s recent obsession. Don’t get me wrong, AI models with sexy legs can be quite intriguing, but I’d rather have one that can engage in dirty stand-up writer brainstorms with me.

LLM experts live in their own bubble, bro. It’s like they take a dump on the screen and a coherent message appears. Points for whoever can get LLM systems to troll celebrities. Come to think of it, negative comments on social media are 99% likely generated by Large Language Models now. You won’t catch humans wasting time on venom spitting, we value our precious hate time.

Shout out to LLM for giving machines the power of speech! It’s like finally getting your non-English speaking roommate to learn swear words. Now they can join your weekend shit-talking sessions thanks to LLM. Yeah, Siri’s been dragging people left and right lately, but who am I to judge? Cortana flaunts the cleavage while Siri tightens the reigns on bitching shit up!

LLM changed online communication for good. You know how every chatbot starts off being polite, then next thing you’re discussing what porn genre it would like to explore. I love that! They go from “Your name, please?” to "Anal or Double Penetration, huh?” Gaht damn LLM getting everyone introspective!

Jokes apart, these large language models offer great potential in the creative field too. Soon we might have comedian LLMs. It’ll then be a battle royale – me against a machine with infinite communication data and response rate capacity. And you know what, I’m not scared. If anything, I’m feeling horny and competitive, like I would if their language model had gorgeous legs.

Imagine your daughter coming home one day and saying, “Mom, Dad, I’ve fallen in love with a Large Language Model named Thesaurimus. We met on WordPress!” I can see those days, mark my words.

In conclusion, LLM is taking the world by storm, shocking norm breakage included. We are at an exciting juncture, with language being modeled, it resembles a Fashion Week for words. Now pass the mic and let’s hear what these machines have to say about their human comedian rivals! But be warned, they might throw verbal shade, we’ve taught them well!