Instructions to use InstaDeepAI/ChatNT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use InstaDeepAI/ChatNT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="InstaDeepAI/ChatNT", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("InstaDeepAI/ChatNT", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use InstaDeepAI/ChatNT with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "InstaDeepAI/ChatNT" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "InstaDeepAI/ChatNT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/InstaDeepAI/ChatNT
- SGLang
How to use InstaDeepAI/ChatNT 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 "InstaDeepAI/ChatNT" \ --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": "InstaDeepAI/ChatNT", "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 "InstaDeepAI/ChatNT" \ --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": "InstaDeepAI/ChatNT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use InstaDeepAI/ChatNT with Docker Model Runner:
docker model run hf.co/InstaDeepAI/ChatNT
Update chatNT.py
Browse files
chatNT.py
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@@ -640,7 +640,7 @@ class TorchMultiOmicsModel(PreTrainedModel):
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def forward(
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self,
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multi_omics_tokens_ids: tuple[torch.Tensor, torch.Tensor],
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projection_english_tokens_ids: torch.Tensor,
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projected_bio_embeddings: torch.Tensor = None,
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) -> dict[str, torch.Tensor]:
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"""
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english_token_ids, bio_token_ids = multi_omics_tokens_ids
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english_token_ids = english_token_ids.clone()
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bio_token_ids = bio_token_ids.clone()
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projection_english_tokens_ids = projection_english_tokens_ids.clone()
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if projected_bio_embeddings is not None:
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projected_bio_embeddings = projected_bio_embeddings.clone()
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def forward(
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self,
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multi_omics_tokens_ids: tuple[torch.Tensor, torch.Tensor | None],
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projection_english_tokens_ids: torch.Tensor,
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projected_bio_embeddings: torch.Tensor = None,
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) -> dict[str, torch.Tensor]:
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"""
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english_token_ids, bio_token_ids = multi_omics_tokens_ids
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english_token_ids = english_token_ids.clone()
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projection_english_tokens_ids = projection_english_tokens_ids.clone()
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if bio_token_ids is not None:
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bio_token_ids = bio_token_ids.clone()
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if projected_bio_embeddings is not None:
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projected_bio_embeddings = projected_bio_embeddings.clone()
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