Instructions to use Eikovo/Otter-1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Eikovo/Otter-1.0 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/meta-llama-3.1-8b-instruct-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "Eikovo/Otter-1.0")

Transformers

How to use Eikovo/Otter-1.0 with Transformers:

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

pipe = pipeline("text-generation", model="Eikovo/Otter-1.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Eikovo/Otter-1.0", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Eikovo/Otter-1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Eikovo/Otter-1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Eikovo/Otter-1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Eikovo/Otter-1.0

SGLang

How to use Eikovo/Otter-1.0 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 "Eikovo/Otter-1.0" \
    --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": "Eikovo/Otter-1.0",
		"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 "Eikovo/Otter-1.0" \
        --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": "Eikovo/Otter-1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use Eikovo/Otter-1.0 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 Eikovo/Otter-1.0 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 Eikovo/Otter-1.0 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Eikovo/Otter-1.0 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Eikovo/Otter-1.0",
    max_seq_length=2048,
)

Docker Model Runner
How to use Eikovo/Otter-1.0 with Docker Model Runner:
```
docker model run hf.co/Eikovo/Otter-1.0
```

Model Card for Model ID

It is for pcb deisgn and EE

Model Details

Trained by an A100 for about 3 days.

Model Description

I trained it off multiple datasets fro pcb design, verilog, and etc it was a solo project and this is the FIRST version of the model.

Developed by: Noah Van Dijk (Eikovo)
Funded by: [More Information Needed]
Model type: Fine-Tuned LLM
Language(s) (NLP): English
License: Apache 2.0

Uses

LLM

Direct Use

LLM

Out-of-Scope Use

Finetuned llm na

[More Information Needed]

Bias, Risks, and Limitations

Should have zero bias, im not responsable for any misuse or bad advice

Recommendations

PCB design

How to Get Started with the Model

Use the code below to get started with the model.

from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained("Eikovo/OTTER-1.0")

Eikovo
/

Otter-1.0

Model Card for Model ID

Model Details

Model Description

Uses

Direct Use

Out-of-Scope Use

Bias, Risks, and Limitations

Recommendations

How to Get Started with the Model

Training Details

Training Procedure

Training Hyperparameters

Evaluation

Testing Data, Factors & Metrics

Testing Data

Factors

Metrics

Results

Summary

Hardware

Software

Contact