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LLM_MODEL_ARCHS = {
"stablelm_epoch": "π΄ StableLM-Epoch",
"stablelm_alpha": "π΄ StableLM-Alpha",
"mixformer-sequential": "π§βπ» Phi Ο",
"RefinedWebModel": "π¦
Falcon",
"gpt_bigcode": "β StarCoder",
"RefinedWeb": "π¦
Falcon",
"baichuan": "π Baichuan ηΎε·", # river
"internlm": "π§βπ InternLM δΉ¦η", # scholar
"mistral": "βοΈ Mistral",
"mixtral": "βοΈ Mixtral",
"codegen": "βΎοΈ CodeGen",
"chatglm": "π¬ ChatGLM",
"falcon": "π¦
Falcon",
"bloom": "πΈ Bloom",
"llama": "π¦ LLaMA",
"rwkv": "π¦ββ¬ RWKV",
"deci": "π΅ deci",
"Yi": "π« Yi δΊΊ", # people
"mpt": "𧱠MPT",
# suggest something
"gpt_neox": "GPT-NeoX",
"gpt_neo": "GPT-Neo",
"gpt2": "GPT-2",
"gptj": "GPT-J",
"xglm": "XGLM",
"bart": "BART",
"opt": "OPT",
}
def model_hyperlink(link, model_name):
return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>'
def process_arch(model_arch):
if model_arch in LLM_MODEL_ARCHS:
return LLM_MODEL_ARCHS[model_arch]
else:
return model_arch
def process_score(score, quantization):
if quantization != "None":
return f"{score:.2f}*"
else:
return f"{score:.2f} "
def process_quantization_scheme(x):
if x["backend.quantization_scheme"] == "bnb" and x["backend.quantization_config.load_in_4bit"] == True:
return "BnB.4bit"
elif x["backend.quantization_scheme"] == "bnb" and x["backend.quantization_config.load_in_8bit"] == True:
return "BnB.8bit"
elif (x["backend.quantization_scheme"] == "gptq") and (
x["backend.quantization_config.exllama_config.version"] == 1
):
return "GPTQ.4bit+ExllamaV1"
elif (x["backend.quantization_scheme"] == "gptq") and (
x["backend.quantization_config.exllama_config.version"] == 2
):
return "GPTQ.4bit+ExllamaV2"
elif x["backend.quantization_scheme"] == "gptq" and x["backend.quantization_config.bits"] == 4:
return "GPTQ.4bit"
else:
return "None"
# def change_tab(query_param):
# query_param = query_param.replace("'", '"')
# query_param = json.loads(query_param)
# if isinstance(query_param, dict) and "tab" in query_param and query_param["tab"] == "plot":
# return gr.Tabs.update(selected=1)
# else:
# return gr.Tabs.update(selected=0)
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