4n3mone/glm-4-ko-9b-chat

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Model Description

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

• —Developed by: 4n3mone (YongSang Yoo)
• —Model type: chatglm
• —Language(s) (NLP): Korean
• —License: glm-4
• —Finetuned from model [optional]: THUDM/glm-4-9b-chat

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• —Repository: THUDM/glm-4-9b-chat
• —Paper [optional]: [More Information Needed]
• —Demo [optional]: [More Information Needed]

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoTokenizer
from vllm import LLM, SamplingParams


# GLM-4-9B-Chat
# If you encounter OOM (Out of Memory) issues, it is recommended to reduce max_model_len or increase tp_size.
max_model_len, tp_size = 131072, 1
model_name = "4n3mone/glm-4-ko-9b-chat"
prompt = [{"role": "user", "content": "피카츄랑 아구몬 중에서 누가 더 귀여워?"}]

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
llm = LLM(
    model=model_name,
    tensor_parallel_size=tp_size,
    max_model_len=max_model_len,
    trust_remote_code=True,
    enforce_eager=True,
    # If you encounter OOM (Out of Memory) issues, it is recommended to enable the following parameters.
    # enable_chunked_prefill=True,
    # max_num_batched_tokens=8192
)
stop_token_ids = [151329, 151336, 151338]
sampling_params = SamplingParams(temperature=0.95, max_tokens=1024, stop_token_ids=stop_token_ids)

inputs = tokenizer.apply_chat_template(prompt, tokenize=False, add_generation_prompt=True)
outputs = llm.generate(prompts=inputs, sampling_params=sampling_params)

print(outputs[0].outputs[0].text)

model.generate(prompt)

logicor benchmark(1-shot)

Training Details

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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