llama3-8b-spaetzle-v39

llama3-8b-spaetzle-v39 is a merge of the following models:

    - cstr/llama3-8b-spaetzle-v38
    - cstr/llama3-8b-spaetzle-v36
    - cstr/llama3-8b-spaetzle-v33
    - cstr/llama3-8b-spaetzle-v28

Evaluation

Quantised as q4km gguf, it achieves

on EQ Bench (v2_de): 65.12 (171 parseable),
on EQ Bench (v2):    73.64 (171 parseable).

Model	AGIEval	GPT4All	TruthfulQA	Bigbench	Average
llama3-8b-spaetzle-v39	43.43	73.31	60	45.89	55.66

GPT4All

Task	Version	Metric	Value		Stderr
arc_challenge	0	acc	60.75	±	1.43
		acc_norm	63.40	±	1.41
arc_easy	0	acc	85.10	±	0.73
		acc_norm	83.46	±	0.76
boolq	1	acc	84.74	±	0.63
hellaswag	0	acc	62.23	±	0.48
		acc_norm	80.73	±	0.39
openbookqa	0	acc	38.00	±	2.17
		acc_norm	46.80	±	2.23
piqa	0	acc	79.92	±	0.93
		acc_norm	80.25	±	0.93
winogrande	0	acc	73.80	±	1.24

Sample output

>>> What is 7777 + 3333?
Let's do the math:

7777 + 3333 = 11110

>>> I have 10 apples. I find 3 gold coins in the bottom of a river. The river runs near a big city that has something to 
... do with what I can spend the coins on. I then lose 4 apples but gain a gold coin. Three birds run into my path and dr
... op 6 apples each. I play an online game and win 6 gold coins but I have to share them equally with my 2 teammates. I 
... buy apples for all the coins I have. The price of an apple is 0.5 coins. How many apples do I have? And where is the 
... river?
Let's break this down step by step!

Initially, you have 10 apples.

You find 3 gold coins in the river.
No mention of spending these coins yet.

You lose 4 apples, leaving you with 6 apples.
You gain a gold coin, so now you have 4 coins (3 + 1).

Then, three birds drop 6 apples each, which is a total of 18 apples. You now have:
6 (initial apples) + 18 (new apples) = 24 apples

In the online game, you win 6 gold coins, but since you have to share with teammates, each of you gets 2 coins (6 ÷
3). So, you gain 2 new coins.

You now have a total of 6 coins (4 initial + 2 gained).

With your 6 coins, you can buy apples at a rate of 0.5 coins per apple.
To find out how many apples you can buy, divide the number of coins by the price per apple:
6 coins ÷ 0.5 coins/apple = 12 apples

So, after buying apples with all your coins, you have a total of 24 (initial) + 12 (new) = 36 apples.

As for where the river is, it runs near a big city that has something to do with spending gold coins. Since you 
bought apples with your coins, we can infer that this city might be a market or a place where fruits and goods are 
sold. However, the exact location of the river is not specified in the story.

>>> Two years ago, I was three times as old as my brother was. In three years, I will be twice as old as my brother. How 
... old is each of us now? 
... 
Let's use variables to represent the current ages:

* My current age = M
* My brother's current age = B

Two years ago, the equation would be:

M - 2 = 3(B - 2)

Since I was three times as old as my brother two years ago, we can simplify the equation:

M - 2 = 3B - 6
M = 3B - 4

Now, let's look at the future situation in three years:

M + 3 = 2(B + 3)

Expanding and simplifying:

M + 3 = 2B + 6
M = 2B + 3

We now have two equations with two variables:

1) M = 3B - 4
2) M = 2B + 3

Equating the right sides of these equations, we get:

3B - 4 = 2B + 3

Simplifying and solving for B (my brother's current age):

B = 7

Now that we know my brother's current age, we can find mine:

M = 3B - 4
= 3(7) - 4
= 21 - 4
= 17

So, I am currently 17 years old, and my brother is currently 7 years old.

🧩 Configuration

models:
    - model: cstr/llama3-8b-spaetzle-v33
    - model: cstr/llama3-8b-spaetzle-v38
    - model: cstr/llama3-8b-spaetzle-v36
    - model: cstr/llama3-8b-spaetzle-v28
merge_method: model_stock
base_model: cstr/llama3-8b-spaetzle-v33
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "cstr/llama3-8b-spaetzle-v39"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])