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diff --git a/ldm/models/diffusion/ddim.py b/ldm/models/diffusion/ddim.py
index c6cfd57..2569ea1 100644
--- a/ldm/models/diffusion/ddim.py
+++ b/ldm/models/diffusion/ddim.py
@@ -25,12 +25,24 @@ class DDIMSampler(object):
         self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
                                                   num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
         alphas_cumprod = self.model.alphas_cumprod
+
+        beta_start = 0.00085

opparco

diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index d2176c9..3735e62 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -13,6 +13,7 @@ include_directories(${CMAKE_CURRENT_SOURCE_DIR})
 if (EMSCRIPTEN)
 else()
     add_subdirectory(main)
+    add_subdirectory(gptneox-wip)
     add_subdirectory(quantize)

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#
# debug tokenizer of lmsys/vicuna-13b-v1.3
#

from transformers import AutoTokenizer


tokenizer = AutoTokenizer.from_pretrained("lmsys/vicuna-13b-v1.3")

def encode_decode(string: str):

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#
# correct vocab of matsuo-lab/weblab-10b
#

vocab = {}

# 95 -> (none)
# 96 -> \xa1
# ...
# 107 -> \xac

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from transformers import AutoTokenizer


tokenizer = AutoTokenizer.from_pretrained("matsuo-lab/weblab-10b")
file_path = "hiragana.txt"
with open(file_path, "w", encoding="utf-8") as o:
    o.write("# unicode decoded [num-ids] id, ...\n")
    for i in range(0x3040, 0x309F + 1):
        ids = tokenizer.encode(chr(i), add_special_tokens=False)
        decoded = tokenizer.decode(ids)

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from transformers import AutoModelForCausalLM, AutoTokenizer


tokenizer = AutoTokenizer.from_pretrained("matsuo-lab/weblab-10b")

dot = tokenizer.encode(".")
print(dot)
# [15]

nemureru = tokenizer.encode("眠れる")

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#
filename = '../grammars/japanese.gbnf'

print(f"overwrite {filename}")
with open(filename, 'w', encoding='utf-8') as f:
    f.write("""#
root ::= char+ ([ \\t\\n] char+)*
char ::= [\u3000-\u303F\u3040-\u309F\u30A0-\u30FF\u4E00-\u9FFF\uFF21-\uFF3A\uFF41-\uFF5A\uFF10-\uFF19]""")

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C:\pub\llama.cpp\build>.\bin\Release\main.exe -m C:\pub\llama\llama-2-7b-chat\ggml-model-q4_K_M.bin -c 2048 -p "translate in japanese: The pen is mightier than the sword."
main: build = 852 (294f424)
main: seed  = 1690113032
llama.cpp: loading model from C:\pub\llama\llama-2-7b-chat\ggml-model-q4_K_M.bin
llama_model_load_internal: format     = ggjt v3 (latest)
llama_model_load_internal: n_vocab    = 32000
llama_model_load_internal: n_ctx      = 2048
llama_model_load_internal: n_embd     = 4096
llama_model_load_internal: n_mult     = 256
llama_model_load_internal: n_head     = 32

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template<typename Key, typename T, T value = T()> 
class defaultable_map : 
	public std::unordered_map<Key, T> 
{ 
public: 
	// inherit std::unordered_map constructors 
	using std::unordered_map<Key, T>::unordered_map; 
 
	T & operator[](const Key & key) 
	{ 

opparco

Here are a few Yuri couples from Madoka Magica, formatted using Markdown:

1. Madoka Kaname and Kyubey
   • Madoka's determination to become a Magical Girl and Kyubey's manipulation of her desires create a complex and often fraught relationship.
2. Homura Akemi and Madoka Kaname
   • Homura's fierce protectiveness of Madoka and her willingness to make sacrifices for her rival's happiness create a powerful and emotional bond.
3. Kyoko Sakura and Sayaka Nakamura
   • Kyoko's quiet strength and Sayaka's desire to save her friend create a touching and heartwarming relationship.
4. Mami Tomoe and Kyoko Sakura
   • Mami's mentorship of Kyoko and Kyoko's admiration for Mami's strength and leadership create a beautiful and supportive relationship.