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

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readme coming soon

Model Details

Model Description

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

Model Sources [optional]

• 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)

Category	Single turn	Multi turn
추론(Reasoning)	6.00	5.57
수학(Math)	5.71	3.00
코딩(Coding)	6.00	5.71
이해(Understanding)	7.71	8.71
글쓰기(Writing)	8.86	7.57
문법(Grammar)	2.86	3.86

Category	Score
Single turn	6.19
Multi turn	5.74
Overall	5.96

Training Details

Training Data

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Training Procedure

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Evaluation

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Summary

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Environmental Impact

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

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Technical Specifications [optional]

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