Instructions to use spow12/EEVE_ver_4.1_sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use spow12/EEVE_ver_4.1_sft with Transformers:

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

pipe = pipeline("text-generation", model="spow12/EEVE_ver_4.1_sft")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("spow12/EEVE_ver_4.1_sft")
model = AutoModelForCausalLM.from_pretrained("spow12/EEVE_ver_4.1_sft", device_map="auto")
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 spow12/EEVE_ver_4.1_sft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "spow12/EEVE_ver_4.1_sft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "spow12/EEVE_ver_4.1_sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/spow12/EEVE_ver_4.1_sft

SGLang

How to use spow12/EEVE_ver_4.1_sft 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 "spow12/EEVE_ver_4.1_sft" \
    --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": "spow12/EEVE_ver_4.1_sft",
		"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 "spow12/EEVE_ver_4.1_sft" \
        --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": "spow12/EEVE_ver_4.1_sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use spow12/EEVE_ver_4.1_sft with Docker Model Runner:
```
docker model run hf.co/spow12/EEVE_ver_4.1_sft
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

ver_4.1_sft

This model is a fine-tuned version of yanolja/EEVE-Korean-10.8B-v1.0 on the Custom dataset.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 8
total_train_batch_size: 8
total_eval_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
training_steps: 0

Training results

Training Loss	Epoch	Step
0.7739	0.2	1124
0.7214	0.4	2248
0.6832	0.6	3372
0.6935	0.8	4496

Framework versions

PEFT 0.10.0
Transformers 4.39.3
Pytorch 2.0.1
Datasets 2.18.0
Tokenizers 0.15.2

Downloads last month: 11

Safetensors

Model size

11B params

Tensor type

BF16

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Quantizations

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Korean LLM

Collection

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