---
library_name: transformers
language:
- ko
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

readme coming soon

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

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]

<!-- Provide the basic links for the model. -->

- **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.
```python
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-preview"
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)

```




## Training Details

### Training Data

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[More Information Needed]

### Training Procedure

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#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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