Instructions to use kaluaim/ChatTS-14B-handler with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kaluaim/ChatTS-14B-handler with Transformers:

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

pipe = pipeline("text-generation", model="kaluaim/ChatTS-14B-handler", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("kaluaim/ChatTS-14B-handler", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use kaluaim/ChatTS-14B-handler with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kaluaim/ChatTS-14B-handler"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kaluaim/ChatTS-14B-handler",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kaluaim/ChatTS-14B-handler

SGLang

How to use kaluaim/ChatTS-14B-handler 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 "kaluaim/ChatTS-14B-handler" \
    --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": "kaluaim/ChatTS-14B-handler",
		"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 "kaluaim/ChatTS-14B-handler" \
        --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": "kaluaim/ChatTS-14B-handler",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use kaluaim/ChatTS-14B-handler with Docker Model Runner:
```
docker model run hf.co/kaluaim/ChatTS-14B-handler
```

ChatTS-14B-handler

File size: 1,292 Bytes

c89a48e

{
  "architectures": [
    "Qwen2TSForCausalLM"
  ],
  "attention_dropout": 0.0,
  "auto_map": {
    "AutoConfig": "configuration_qwen2.Qwen2TSConfig",
    "AutoModel": "modeling_qwen2.Qwen2TSForCausalLM",
    "AutoModelForCausalLM": "modeling_qwen2.Qwen2TSForCausalLM",
    "AutoProcessor": "processing_qwen2_ts.Qwen2TSProcessor"
  },
  "bos_token_id": 151643,
  "eos_token_id": 151645,
  "hidden_act": "silu",
  "hidden_size": 5120,
  "ignore_index": -100,
  "initializer_range": 0.02,
  "intermediate_size": 13824,
  "max_position_embeddings": 32768,
  "max_window_layers": 70,
  "model_type": "chatts",
  "num_attention_heads": 40,
  "num_hidden_layers": 48,
  "num_key_value_heads": 8,
  "pad_token_id": 151643,
  "rms_norm_eps": 1e-06,
  "rope_theta": 1000000.0,
  "sliding_window": 131072,
  "tie_word_embeddings": false,
  "torch_dtype": "float16",
  "transformers_version": "4.52.4",
  "ts": {
    "embedding_dim": 16,
    "hidden_size": 5120,
    "max_length": 32768,
    "max_sequence_length": 32768,
    "num_features": 2,
    "num_layers": 5,
    "patch_size": 8,
    "use_position_embedding": true,
    "use_position_idx": false
  },
  "ts_token_end_index": 151666,
  "ts_token_start_index": 151665,
  "use_cache": false,
  "use_sliding_window": false,
  "vocab_size": 152064
}