Instructions to use PascalNotin/Tranception_Large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PascalNotin/Tranception_Large with Transformers:

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

pipe = pipeline("text-generation", model="PascalNotin/Tranception_Large")

# Load model directly
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("PascalNotin/Tranception_Large")
model = AutoModelWithLMHead.from_pretrained("PascalNotin/Tranception_Large")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use PascalNotin/Tranception_Large with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PascalNotin/Tranception_Large"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PascalNotin/Tranception_Large",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/PascalNotin/Tranception_Large

SGLang

How to use PascalNotin/Tranception_Large 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 "PascalNotin/Tranception_Large" \
    --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": "PascalNotin/Tranception_Large",
		"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 "PascalNotin/Tranception_Large" \
        --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": "PascalNotin/Tranception_Large",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use PascalNotin/Tranception_Large with Docker Model Runner:
```
docker model run hf.co/PascalNotin/Tranception_Large
```

Tranception_Large

File size: 1,198 Bytes

ca31f7c

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  "MSA_filename": null,
  "MSA_start": null,
  "MSA_weight_file_name": null,
  "_name_or_path": "Tranception_Large",
  "activation_function": "squared_relu",
  "architectures": [
    "TranceptionLMHeadModel"
  ],
  "attention_mode": "tranception",
  "attn_pdrop": 0.1,
  "bos_token_id": 1,
  "clustal_omega_location": null,
  "embd_pdrop": 0.1,
  "eos_token_id": 2,
  "full_protein_length": null,
  "initializer_range": 0.02,
  "layer_norm_epsilon": 1e-05,
  "local_batch_size": 1,
  "model_type": "gpt2",
  "n_ctx": 1024,
  "n_embd": 1280,
  "n_head": 20,
  "n_inner": 5120,
  "n_layer": 36,
  "n_positions": 1024,
  "position_embedding": "grouped_alibi",
  "reorder_and_upcast_attn": false,
  "resid_pdrop": 0.1,
  "retrieval_aggregation_mode": null,
  "retrieval_inference_weight": 0.6,
  "scale_attn_by_inverse_layer_idx": false,
  "scale_attn_weights": true,
  "scoring_window": "optimal",
  "summary_activation": null,
  "summary_first_dropout": 0.1,
  "summary_proj_to_labels": true,
  "summary_type": "cls_index",
  "summary_use_proj": true,
  "tokenizer": null,
  "torch_dtype": "float32",
  "transformers_version": "4.17.0",
  "use_cache": true,
  "vocab_size": 25
}