Instructions to use joelhenwang/OdinNext-138M-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use joelhenwang/OdinNext-138M-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="joelhenwang/OdinNext-138M-Instruct", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("joelhenwang/OdinNext-138M-Instruct", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use joelhenwang/OdinNext-138M-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "joelhenwang/OdinNext-138M-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "joelhenwang/OdinNext-138M-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/joelhenwang/OdinNext-138M-Instruct

SGLang

How to use joelhenwang/OdinNext-138M-Instruct 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 "joelhenwang/OdinNext-138M-Instruct" \
    --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": "joelhenwang/OdinNext-138M-Instruct",
		"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 "joelhenwang/OdinNext-138M-Instruct" \
        --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": "joelhenwang/OdinNext-138M-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use joelhenwang/OdinNext-138M-Instruct with Docker Model Runner:
```
docker model run hf.co/joelhenwang/OdinNext-138M-Instruct
```

OdinNext-138M-Instruct / tokenizer_config.json

joelhenwang

OdinNext-138M-Instruct: SFT + LFM2.5 SeqKD

8d6741e verified 2 days ago

raw

history blame contribute delete

470 Bytes

	{
	"model_max_length": 2048,
	"padding_side": "right",
	"tokenizer_class": "PreTrainedTokenizerFast",
	"clean_up_tokenization_spaces": false,
	"bos_token": null,
	"eos_token": "<\|im_end\|>",
	"pad_token": "<\|im_end\|>",
	"unk_token": null,
	"chat_template": "{% for message in messages %}{{ '<\|im_start\|>' + message['role'] + '\n' + message['content'] + '<\|im_end\|>' + '\n' }}{% endfor %}{% if add_generation_prompt %}{{ '<\|im_start\|>assistant\n' }}{% endif %}"
	}