Text Generation
Transformers
Safetensors
taonet
trust-remote-code
sentencepiece
custom-architecture
custom_code
Instructions to use TaoTern/TaoNet-mini-A2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TaoTern/TaoNet-mini-A2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TaoTern/TaoNet-mini-A2", trust_remote_code=True, device_map="auto")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("TaoTern/TaoNet-mini-A2", trust_remote_code=True, dtype="auto", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use TaoTern/TaoNet-mini-A2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TaoTern/TaoNet-mini-A2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TaoTern/TaoNet-mini-A2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/TaoTern/TaoNet-mini-A2
- SGLang
How to use TaoTern/TaoNet-mini-A2 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 "TaoTern/TaoNet-mini-A2" \ --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": "TaoTern/TaoNet-mini-A2", "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 "TaoTern/TaoNet-mini-A2" \ --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": "TaoTern/TaoNet-mini-A2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use TaoTern/TaoNet-mini-A2 with Docker Model Runner:
docker model run hf.co/TaoTern/TaoNet-mini-A2
Upload folder using huggingface_hub
Browse files- config.json +4 -0
- export_to_hf.py +5 -0
config.json
CHANGED
|
@@ -2,6 +2,10 @@
|
|
| 2 |
"architectures": [
|
| 3 |
"TaoNetForCausalLM"
|
| 4 |
],
|
|
|
|
|
|
|
|
|
|
|
|
|
| 5 |
"bos_token_id": 1,
|
| 6 |
"cnn_channels": [
|
| 7 |
32,
|
|
|
|
| 2 |
"architectures": [
|
| 3 |
"TaoNetForCausalLM"
|
| 4 |
],
|
| 5 |
+
"auto_map": {
|
| 6 |
+
"AutoConfig": "configuration_taonet.TaoNetConfig",
|
| 7 |
+
"AutoModelForCausalLM": "modeling_taonet.TaoNetForCausalLM"
|
| 8 |
+
},
|
| 9 |
"bos_token_id": 1,
|
| 10 |
"cnn_channels": [
|
| 11 |
32,
|
export_to_hf.py
CHANGED
|
@@ -53,6 +53,11 @@ def main():
|
|
| 53 |
eos_token_id=special_tokens.get("<EOS>", 2),
|
| 54 |
unk_token_id=special_tokens.get("<UNK>", 0),
|
| 55 |
)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 56 |
|
| 57 |
model = TaoNetForCausalLM(hf_config)
|
| 58 |
missing, unexpected = model.model.load_state_dict(model_state, strict=False)
|
|
|
|
| 53 |
eos_token_id=special_tokens.get("<EOS>", 2),
|
| 54 |
unk_token_id=special_tokens.get("<UNK>", 0),
|
| 55 |
)
|
| 56 |
+
hf_config.architectures = ["TaoNetForCausalLM"]
|
| 57 |
+
hf_config.auto_map = {
|
| 58 |
+
"AutoConfig": "configuration_taonet.TaoNetConfig",
|
| 59 |
+
"AutoModelForCausalLM": "modeling_taonet.TaoNetForCausalLM",
|
| 60 |
+
}
|
| 61 |
|
| 62 |
model = TaoNetForCausalLM(hf_config)
|
| 63 |
missing, unexpected = model.model.load_state_dict(model_state, strict=False)
|