Instructions to use DopeorNope/COKAL-v1-70B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DopeorNope/COKAL-v1-70B with Transformers:

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

pipe = pipeline("text-generation", model="DopeorNope/COKAL-v1-70B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DopeorNope/COKAL-v1-70B")
model = AutoModelForCausalLM.from_pretrained("DopeorNope/COKAL-v1-70B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use DopeorNope/COKAL-v1-70B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DopeorNope/COKAL-v1-70B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DopeorNope/COKAL-v1-70B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DopeorNope/COKAL-v1-70B

SGLang

How to use DopeorNope/COKAL-v1-70B 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 "DopeorNope/COKAL-v1-70B" \
    --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": "DopeorNope/COKAL-v1-70B",
		"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 "DopeorNope/COKAL-v1-70B" \
        --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": "DopeorNope/COKAL-v1-70B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DopeorNope/COKAL-v1-70B with Docker Model Runner:
```
docker model run hf.co/DopeorNope/COKAL-v1-70B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🐻‍❄️COKAL-v1_70B🐻‍❄️

Model Details

Model Developers Seungyoo Lee (DopeorNope)

Input Models input text only.

Output Models generate text only.

Model Architecture
COKAL-v1_70B is an auto-regressive 70B language model based on the LLaMA2 transformer architecture.

Base Model

Training Dataset

SFT training dataset: garage-bAInd/Open-Platypus

Training
I developed the model in an environment with A100 x 8

Implementation Code


from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

repo = "DopeorNope/COKAL-v1_70B"
model = AutoModelForCausalLM.from_pretrained(
        repo,
        return_dict=True,
        torch_dtype=torch.float16,
        device_map='auto'
)
model_tokenizer = AutoTokenizer.from_pretrained(repo)

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Safetensors

Model size

69B params

Tensor type

F32

Model tree for DopeorNope/COKAL-v1-70B

Quantizations

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COKAL-v1-70B

🐻‍❄️COKAL-v1_70B🐻‍❄️

Model Details

Implementation Code

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