Instructions to use Neetree/KoLama with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Neetree/KoLama with Transformers:

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

pipe = pipeline("text-generation", model="Neetree/KoLama")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Neetree/KoLama")
model = AutoModelForCausalLM.from_pretrained("Neetree/KoLama")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Neetree/KoLama with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Neetree/KoLama

SGLang

How to use Neetree/KoLama 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 "Neetree/KoLama" \
    --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": "Neetree/KoLama",
		"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 "Neetree/KoLama" \
        --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": "Neetree/KoLama",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio

How to use Neetree/KoLama with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Neetree/KoLama to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Neetree/KoLama to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Neetree/KoLama to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Neetree/KoLama",
    max_seq_length=2048,
)

Docker Model Runner
How to use Neetree/KoLama with Docker Model Runner:
```
docker model run hf.co/Neetree/KoLama
```

KoLama / README.md

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	---
	base_model: unsloth/Meta-Llama-3.1-8B-bnb-4bit
	tags:
	- text-generation-inference
	- transformers
	- unsloth
	- llama
	- trl
	- sft
	license: apache-2.0
	language:
	- en
	datasets:
	- Neetree/raw_enko_opus_CCM
	---


	# KoLama: Fine-Tuned Llama3.1-8B Model

	## Overview
	KoLama is a fine-tuned version of the Meta-Llama-3.1-8B-bnb-4bit model, developed by Neetree. This model was trained using the [Unsloth](https://github.com/unslothai/unsloth) library, which significantly accelerated the training process, and Huggingface's TRL (Transformer Reinforcement Learning) library. The model is optimized for text generation tasks and is licensed under Apache-2.0.

	## Model Details
	- Base Model: [unsloth/Meta-Llama-3.1-8B-bnb-4bit](https://huggingface.co/unsloth/Meta-Llama-3.1-8B-bnb-4bit)
	- Fine-Tuned by: Neetree
	- License: Apache-2.0
	- Language: English
	- Training Dataset: [Neetree/raw_enko_opus_CCM](https://huggingface.co/datasets/Neetree/raw_enko_opus_CCM)

	## Key Features
	- Efficient Training: The model was trained 2x faster using Unsloth, making the fine-tuning process more efficient.
	- Text Generation: Optimized for text generation tasks, leveraging the power of the Llama3.1 architecture.
	- Reinforcement Learning: Fine-tuned using Huggingface's TRL library, which incorporates reinforcement learning techniques to improve model performance.

	## Usage
	To use KoLama for text generation, you can load the model using the `transformers` library:

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_name = "Neetree/KoLama"
	model = AutoModelForCausalLM.from_pretrained(model_name)
	tokenizer = AutoTokenizer.from_pretrained(model_name)

	input_text = "Once upon a time"
	inputs = tokenizer(input_text, return_tensors="pt")

	# Generate text
	outputs = model.generate(**inputs, max_length=50)
	generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)

	print(generated_text)
	```

	## Training Details
	- Training Speed: 2x faster training using Unsloth.
	- Fine-Tuning Method: Supervised Fine-Tuning (SFT) with reinforcement learning via Huggingface's TRL library.
	- Dataset: The model was fine-tuned on the [Neetree/raw_enko_opus_CCM](https://huggingface.co/datasets/Neetree/raw_enko_opus_CCM) dataset, which contains English-Korean parallel text data.

	## License
	This model is licensed under the Apache-2.0 license. For more details, please refer to the [LICENSE](https://www.apache.org/licenses/LICENSE-2.0) file.

	## Acknowledgments
	- Unsloth: For providing the tools to accelerate the training process.
	- Huggingface: For the TRL library and the transformers framework.
	- Meta: For the original Llama3.1-8B model.

	[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)