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
```

Parveshiiii commited on Mar 30

Commit

050ef42

verified ·

1 Parent(s): cd9963e

Update modelling_model.py

Browse files

Files changed (1) hide show

modelling_model.py +4 -1

modelling_model.py CHANGED Viewed

@@ -8,7 +8,10 @@ from transformers.modeling_outputs import CausalLMOutputWithPast, BaseModelOutpu
 from typing import Optional, Tuple, List, Union
 import inspect
 from dataclasses import dataclass
-from configuration_model import HybridModelConfig
 def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0):
     freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))

 from typing import Optional, Tuple, List, Union
 import inspect
 from dataclasses import dataclass
+try:
+    from .configuration_model import HybridModelConfig
+except ImportError:
+    from configuration_model import HybridModelConfig
 def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0):
     freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))