Instructions to use kuotient/EEVE-Instruct-Math-10.8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kuotient/EEVE-Instruct-Math-10.8B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="kuotient/EEVE-Instruct-Math-10.8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kuotient/EEVE-Instruct-Math-10.8B")
model = AutoModelForCausalLM.from_pretrained("kuotient/EEVE-Instruct-Math-10.8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kuotient/EEVE-Instruct-Math-10.8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kuotient/EEVE-Instruct-Math-10.8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kuotient/EEVE-Instruct-Math-10.8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kuotient/EEVE-Instruct-Math-10.8B

SGLang

How to use kuotient/EEVE-Instruct-Math-10.8B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "kuotient/EEVE-Instruct-Math-10.8B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kuotient/EEVE-Instruct-Math-10.8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "kuotient/EEVE-Instruct-Math-10.8B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kuotient/EEVE-Instruct-Math-10.8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use kuotient/EEVE-Instruct-Math-10.8B with Docker Model Runner:
```
docker model run hf.co/kuotient/EEVE-Instruct-Math-10.8B
```

EEVE-Instruct-Math-10.8B

File size: 3,127 Bytes

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---
model-index:
- name: EEVE-Instruct-Math-10.8B
  results:
  - task:
      type: text-generation
    dataset:
      name: gsm8k-ko
      type: gsm8k
    metrics:
    - name: pass@1
      type: pass@1
      value: 0.4845
      verified: false
base_model:
- yanolja/EEVE-Korean-Instruct-10.8B-v1.0
- kuotient/EEVE-Math-10.8B-SFT
tags:
- merge
license: cc-by-sa-4.0
language:
- ko
---
# EEVE-Instruct-Math-10.8B

`EEVE-Math` 프로젝트는
- Orca-Math-200k 번역 ([Orca-Math: Unlocking the potential of SLMs in Grade School Math](https://arxiv.org/pdf/2402.14830.pdf))
- gsm8k 번역, lm_eval 활용
- Mergekit을 이용한 dare-ties 사용 ([DARE](https://arxiv.org/abs/2311.03099))

에 대한 내용을 포괄하고 있습니다.

> 이 모델은 EEVE-Math와 EEVE-Instruct의 dare-ties로 병합한 병합 모델입니다. 이 프로젝트는 이런 과정을 통해 특화 모델의 EEVE-Math의 성능을 많이 잃지 않고 Instruct 모델의 사용성을 유지할 수 있음을 보여주는 Proof of concept의 성격을 가지고 있습니다.

| Model | gsm8k-ko(pass@1) |
|---|---|
| EEVE(Base) | 0.4049 |
| [EEVE-Math](https://huggingface.co/kuotient/EEVE-Math-10.8B) (epoch 1) | 0.508 |
| EEVE-Math (epoch 2) | **0.539** |
| [EEVE-Instruct](https://huggingface.co/yanolja/EEVE-Korean-Instruct-10.8B-v1.0) | 0.4511 |
| EEVE-Instruct + Math | **0.4845** |

## Merge Details
This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [yanolja/EEVE-Korean-Instruct-10.8B-v1.0](https://huggingface.co/yanolja/EEVE-Korean-Instruct-10.8B-v1.0) as a base.

### Models Merged

The following models were included in the merge:
* [kuotient/EEVE-Math-10.8B](https://huggingface.co/kuotient/EEVE-Math-10.8B)

### Configuration

The following YAML configuration was used to produce this model:

```yaml
models:
  - model: yanolja/EEVE-Korean-10.8B-v1.0
    # no parameters necessary for base model
  - model: yanolja/EEVE-Korean-Instruct-10.8B-v1.0
    parameters:
      density: 0.53
      weight: 0.6
  - model: kuotient/EEVE-Math-10.8B
    parameters:
      density: 0.53
      weight: 0.4
merge_method: dare_ties
base_model: yanolja/EEVE-Korean-10.8B-v1.0
parameters:
  int8_mask: true
dtype: bfloat16
```

## Evaluation
[gsm8k-ko](https://huggingface.co/datasets/kuotient/gsm8k-ko), kobest
```
git clone https://github.com/kuotient/lm-evaluation-harness.git
cd lm-evaluation-harness
pip install -e .
```
```
lm_eval --model hf \
    --model_args pretrained=yanolja/EEVE-Korean-Instruct-2.8B-v1.0 \
    --tasks gsm8k-ko \
    --device cuda:0 \
    --batch_size auto:4
```

| Model | gsm8k(pass@1) | boolq(acc) | copa(acc) | hellaswag(acc) | Overall |
|---|---|---|---|---|---|
| yanolja/EEVE-Korean-10.8B-v1.0 | 0.4049 | - | - | - | - | - |
| yanolja/EEVE-Korean-Instruct-10.8B-v1.0 | 0.4511 | **0.8668** | **0.7450** | 0.4940 | 0.6392 |
| [**EEVE-Math-10.8B**](https://huggingface.co/kuotient/EEVE-Math-10.8B) | **0.5390** | 0.8027 | 0.7260 | 0.4760 | 0.6359 |
| **EEVE-Instruct-Math-10.8B** | 0.4845 | 0.8519 | 0.7410 | **0.4980** | **0.6439** |