Quantization made by Richard Erkhov.

K2-Chat - GGUF

Model creator: https://huggingface.co/LLM360/
Original model: https://huggingface.co/LLM360/K2-Chat/

Name	Quant method	Size
K2-Chat.Q2_K.gguf	Q2_K	22.46GB
K2-Chat.IQ3_XS.gguf	IQ3_XS	24.81GB
K2-Chat.IQ3_S.gguf	IQ3_S	26.23GB
K2-Chat.Q3_K_S.gguf	Q3_K_S	26.23GB
K2-Chat.IQ3_M.gguf	IQ3_M	27.78GB
K2-Chat.Q3_K.gguf	Q3_K	29.46GB
K2-Chat.Q3_K_M.gguf	Q3_K_M	29.46GB
K2-Chat.Q3_K_L.gguf	Q3_K_L	32.27GB
K2-Chat.IQ4_XS.gguf	IQ4_XS	32.64GB
K2-Chat.Q4_0.gguf	Q4_0	34.27GB
K2-Chat.IQ4_NL.gguf	IQ4_NL	34.48GB
K2-Chat.Q4_K_S.gguf	Q4_K_S	34.51GB
K2-Chat.Q4_K.gguf	Q4_K	36.65GB
K2-Chat.Q4_K_M.gguf	Q4_K_M	36.65GB
K2-Chat.Q4_1.gguf	Q4_1	38.05GB
K2-Chat.Q5_0.gguf	Q5_0	41.84GB
K2-Chat.Q5_K_S.gguf	Q5_K_S	41.84GB
K2-Chat.Q5_K.gguf	Q5_K	43.06GB
K2-Chat.Q5_K_M.gguf	Q5_K_M	43.06GB
K2-Chat.Q5_1.gguf	Q5_1	45.62GB
K2-Chat.Q6_K.gguf	Q6_K	49.88GB
K2-Chat.Q8_0.gguf	Q8_0	64.61GB

Original model description:

license: apache-2.0

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.

Open LLM Leaderboard

Evaluation	Score	Raw Score
IFEval	51.52	52
BBH	33.79	54
Math Lvl 5	1.59	2
GPQA	7.49	31
MUSR	16.82	46
MMLU-PRO	26.34	34
Average	22.93	36.5

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])

LLM360 Developer Suite

We provide step-by-step finetuning tutorials for tech enthusiasts, AI practitioners and academic or industry researchers here.

About LLM360

LLM360 is an open research lab enabling community-owned AGI through open-source large model research and development.

LLM360 enables community-owned AGI by creating standards and tools to advance the bleeding edge of LLM capability and empower knowledge transfer, research, and development.

We believe in a future where artificial general intelligence (AGI) is created by the community, for the community. Through an open ecosystem of equitable computational resources, high quality data, and flowing technical knowledge, we can ensure ethical AGI development and universal access for all innovators.

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Citation

BibTeX:

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