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 / README.md

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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 \|