K2-Chat: a fully-reproducible large language model outperforming Llama 2 70B Chat using 35% less compute

K2 Chat is finetuned from K2-65B. K2 Chat outperforms Llama 2-70B-Chat on all evaluations conducted. The model also outperforms Llama 3-70B-Instruct on coding tasks.

LLM360 Model Performance and Evaluation Collection

The LLM360 Performance and Evaluation Collection is a robust evaluations set consisting of general and domain specific evaluations to assess model knowledge and function.

Evaluations include standard best practice benchmarks, medical, math, and coding knowledge. More about the evaluations can be found here.

Datasets and Mix

Subset	#Tokens	Avg. #Q	Avg. Query Len	Avg. #R	Avg. Reply Len
MathInstruct	66,639,699	1.00	81.53	1.00	172.78
OpenHermes-2	404,820,694	1.01	152.38	1.01	249.12
FLAN_3M	2,346,961,387	1.00	727.49	1.00	54.83
Standford Encyclopedia Philosophy	786,928	1.00	219.09	1.00	166.28
TinyStories	1,448,898	1.00	260.82	1.00	207.47
Safety & Alignment Data	99,976,621	1.00	126.71	1.00	373.79
Total	2,920,634,227

Loading K2-Chat

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("LLM360/K2-Chat")
model = AutoModelForCausalLM.from_pretrained("LLM360/K2-Chat")

prompt = '<|beginofuser|>what is the highest mountain on earth?<|beginofsystem|>'

input_ids = tokenizer(prompt, return_tensors="pt").input_ids
gen_tokens = model.generate(input_ids, do_sample=True, max_new_tokens=128)

print("-"*20 + "Output for model"  + 20 * '-')
print(tokenizer.batch_decode(gen_tokens)[0])

Alternatively, you can construct the prompt by applying the chat template of tokenizer on input conversation:

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("LLM360/K2-Chat")
model = AutoModelForCausalLM.from_pretrained("LLM360/K2-Chat")

messages = [{"role": "user", "content": "what is the highest mountain on earth?"}]

input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
gen_tokens = model.generate(input_ids, do_sample=True, max_new_tokens=128)

print("-"*20 + "Output for model"  + 20 * '-')
print(tokenizer.batch_decode(gen_tokens)[0])

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Citation

BibTeX:

@article{
      title={LLM360 K2-65B: Scaling Up Fully Transparent Open-Source LLMs}, 
      author={The LLM360 Team},
      year={2024},
}