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 / config.json

Parveshiiii

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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
	}