Instructions to use openeurollm/kda-sigmoid-hybrid-1.3B-100B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use openeurollm/kda-sigmoid-hybrid-1.3B-100B with Transformers:

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

pipe = pipeline("text-generation", model="openeurollm/kda-sigmoid-hybrid-1.3B-100B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("openeurollm/kda-sigmoid-hybrid-1.3B-100B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use openeurollm/kda-sigmoid-hybrid-1.3B-100B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "openeurollm/kda-sigmoid-hybrid-1.3B-100B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openeurollm/kda-sigmoid-hybrid-1.3B-100B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/openeurollm/kda-sigmoid-hybrid-1.3B-100B

SGLang

How to use openeurollm/kda-sigmoid-hybrid-1.3B-100B 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 "openeurollm/kda-sigmoid-hybrid-1.3B-100B" \
    --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": "openeurollm/kda-sigmoid-hybrid-1.3B-100B",
		"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 "openeurollm/kda-sigmoid-hybrid-1.3B-100B" \
        --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": "openeurollm/kda-sigmoid-hybrid-1.3B-100B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use openeurollm/kda-sigmoid-hybrid-1.3B-100B with Docker Model Runner:
```
docker model run hf.co/openeurollm/kda-sigmoid-hybrid-1.3B-100B
```

kda-sigmoid-hybrid-1.3B-100B / tokenizer_config.json

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Add ComplexKDA 1.3B/100BT FineWeb-Edu model

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	{
	"add_bos_token": false,
	"add_eos_token": false,
	"bos_token": {
	"__type": "AddedToken",
	"content": "</s>",
	"lstrip": false,
	"normalized": true,
	"rstrip": false,
	"single_word": false
	},
	"clean_up_tokenization_spaces": false,
	"chat_template": "{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 and system_message != false %}{% set content = '<<SYS>>\\n' + system_message + '\\n<</SYS>>\\n\\n' + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ bos_token + '[INST] ' + content.strip() + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ ' ' + content.strip() + ' ' + eos_token }}{% endif %}{% endfor %}",
	"eos_token": {
	"__type": "AddedToken",
	"content": "</s>",
	"lstrip": false,
	"normalized": true,
	"rstrip": false,
	"single_word": false
	},
	"model_max_length": 4096,
	"pad_token": null,
	"sp_model_kwargs": {},
	"tokenizer_class": "LlamaTokenizer",
	"unk_token": {
	"__type": "AddedToken",
	"content": "<unk>",
	"lstrip": false,
	"normalized": true,
	"rstrip": false,
	"single_word": false
	}
	}