Instructions to use tlphams/gollm-12.8b-instruct-v2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tlphams/gollm-12.8b-instruct-v2.0 with Transformers:

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

pipe = pipeline("text-generation", model="tlphams/gollm-12.8b-instruct-v2.0")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tlphams/gollm-12.8b-instruct-v2.0")
model = AutoModelForCausalLM.from_pretrained("tlphams/gollm-12.8b-instruct-v2.0")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use tlphams/gollm-12.8b-instruct-v2.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tlphams/gollm-12.8b-instruct-v2.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tlphams/gollm-12.8b-instruct-v2.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/tlphams/gollm-12.8b-instruct-v2.0

SGLang

How to use tlphams/gollm-12.8b-instruct-v2.0 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 "tlphams/gollm-12.8b-instruct-v2.0" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tlphams/gollm-12.8b-instruct-v2.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "tlphams/gollm-12.8b-instruct-v2.0" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tlphams/gollm-12.8b-instruct-v2.0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use tlphams/gollm-12.8b-instruct-v2.0 with Docker Model Runner:
```
docker model run hf.co/tlphams/gollm-12.8b-instruct-v2.0
```

gollm-instruct-all-in-one-v1

This model is a fine-tuned version of EleutherAI/polyglot-ko-12.8b on a custom mixed dataset

Model description

No-context template

아래는 작업을 설명하는 질문어와 추가 컨텍스트를 제공하는 맥락이 함께 제공됩니다. 요청을 적절히 완료하는 답변을 작성하세요.

### 질문:
{instruction}

### 답변:

With context template

아래는 작업을 설명하는 질문어와 추가 컨텍스트를 제공하는 맥락이 함께 제공됩니다. 요청을 적절히 완료하는 답변을 작성하세요.

### 맥락:
{input}

### 질문:
{instruction}

### 답변:

Intended uses & limitations

More information needed

Training and evaluation data

self-introduction (20 samples)
Combined KoAlpaca and KULLM - no-context samples only (145.8k samples)
- KoAlpaca v1.0
- KoAlpaca v1.1
- KULLM (Dolly and Vicuna only)
Naver news summarization (22.2k samples)
KLUE MRC (17.5k samples)
KLUE STS (5.6k samples)

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 2
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 8
saved_checkpoint_at_epoch: 4 (condition: loss < 0.3)

Training results

Training Loss	Epoch	Step
1.5688	1.0	11947
1.0424	2.0	23895
0.5542	3.0	35843
0.2548	4.0	47791
0.1479	5.0	59738

Framework versions

Transformers 4.32.0.dev0
Pytorch 2.0.0+cu117
Datasets 2.11.0
Tokenizers 0.13.3

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Model tree for tlphams/gollm-12.8b-instruct-v2.0

Base model

EleutherAI/polyglot-ko-12.8b

Finetuned

(11)

this model

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gollm-12.8b-instruct-v2.0