Instructions to use Parveshiiii/Terminator-X with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Parveshiiii/Terminator-X with Transformers:

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

pipe = pipeline("text-generation", model="Parveshiiii/Terminator-X", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Parveshiiii/Terminator-X", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Parveshiiii/Terminator-X with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Parveshiiii/Terminator-X

SGLang

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

Docker Model Runner
How to use Parveshiiii/Terminator-X with Docker Model Runner:
```
docker model run hf.co/Parveshiiii/Terminator-X
```

Terminator-X

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{
  "architectures": [
    "HybridForCausalLM"
  ],
  "attention_dropout": 0.0,
  "auto_map": {
    "AutoConfig": "configuration_model.HybridModelConfig",
    "AutoModelForCausalLM": "modelling_model.HybridForCausalLM"
  },
  "bos_token_id": 151643,
  "dtype": "float32",
  "eos_token_id": 248046,
  "hidden_act": "silu",
  "hidden_size": 768,
  "initializer_range": 0.02,
  "intermediate_size": 2048,
  "kv_lora_rank": 192,
  "max_position_embeddings": 32768,
  "mhc_alpha_init": 0.01,
  "mhc_num_streams": 4,
  "mhc_readout_init": "first",
  "mhc_rmsnorm_eps": 1e-06,
  "mhc_sinkhorn_iters": 20,
  "mhc_stream_init": "paper",
  "model_type": "hybrid_model",
  "num_attention_heads": 12,
  "num_hidden_layers": 12,
  "pad_token_id": 248044,
  "q_lora_rank": 384,
  "qk_rope_head_dim": 32,
  "rms_norm_eps": 1e-06,
  "rope_theta": 10000.0,
  "sliding_window": 4096,
  "tie_word_embeddings": false,
  "transformers_version": "5.0.0",
  "use_cache": true,
  "vocab_size": 248077
}