Instructions to use N8Programs/NextTerm-2-440M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use N8Programs/NextTerm-2-440M with Transformers:

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

pipe = pipeline("text-generation", model="N8Programs/NextTerm-2-440M")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("N8Programs/NextTerm-2-440M")
model = AutoModelForCausalLM.from_pretrained("N8Programs/NextTerm-2-440M", device_map="auto")

MLX

How to use N8Programs/NextTerm-2-440M with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm
# if on a CUDA device, also pip install mlx[cuda]

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("N8Programs/NextTerm-2-440M")

prompt = "Once upon a time in"
text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

vLLM

How to use N8Programs/NextTerm-2-440M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "N8Programs/NextTerm-2-440M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "N8Programs/NextTerm-2-440M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/N8Programs/NextTerm-2-440M

SGLang

How to use N8Programs/NextTerm-2-440M 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 "N8Programs/NextTerm-2-440M" \
    --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": "N8Programs/NextTerm-2-440M",
		"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 "N8Programs/NextTerm-2-440M" \
        --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": "N8Programs/NextTerm-2-440M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

MLX LM

How to use N8Programs/NextTerm-2-440M with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Generate some text
mlx_lm.generate --model "N8Programs/NextTerm-2-440M" --prompt "Once upon a time"

Docker Model Runner
How to use N8Programs/NextTerm-2-440M with Docker Model Runner:
```
docker model run hf.co/N8Programs/NextTerm-2-440M
```
Atomic Chat

NextTerm-2-440M

File size: 594 Bytes

40cfd23

{
  "architectures": [
    "Qwen3ForCausalLM"
  ],
  "model_type": "qwen3",
  "vocab_size": 259,
  "hidden_size": 1024,
  "num_hidden_layers": 28,
  "intermediate_size": 3072,
  "num_attention_heads": 16,
  "num_key_value_heads": 8,
  "head_dim": 128,
  "hidden_act": "silu",
  "rms_norm_eps": 1e-06,
  "max_position_embeddings": 4096,
  "rope_theta": 1000000.0,
  "rope_scaling": null,
  "attention_bias": false,
  "attention_dropout": 0.0,
  "tie_word_embeddings": false,
  "use_cache": true,
  "bos_token_id": 256,
  "eos_token_id": 257,
  "pad_token_id": 258,
  "torch_dtype": "bfloat16"
}