Instructions to use T3Q-LLM/T3Q-LLM-TE-NLI-Lora16-v1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use T3Q-LLM/T3Q-LLM-TE-NLI-Lora16-v1.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="T3Q-LLM/T3Q-LLM-TE-NLI-Lora16-v1.0") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("T3Q-LLM/T3Q-LLM-TE-NLI-Lora16-v1.0") model = AutoModelForCausalLM.from_pretrained("T3Q-LLM/T3Q-LLM-TE-NLI-Lora16-v1.0") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use T3Q-LLM/T3Q-LLM-TE-NLI-Lora16-v1.0 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "T3Q-LLM/T3Q-LLM-TE-NLI-Lora16-v1.0" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "T3Q-LLM/T3Q-LLM-TE-NLI-Lora16-v1.0", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/T3Q-LLM/T3Q-LLM-TE-NLI-Lora16-v1.0
- SGLang
How to use T3Q-LLM/T3Q-LLM-TE-NLI-Lora16-v1.0 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 "T3Q-LLM/T3Q-LLM-TE-NLI-Lora16-v1.0" \ --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": "T3Q-LLM/T3Q-LLM-TE-NLI-Lora16-v1.0", "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 "T3Q-LLM/T3Q-LLM-TE-NLI-Lora16-v1.0" \ --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": "T3Q-LLM/T3Q-LLM-TE-NLI-Lora16-v1.0", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use T3Q-LLM/T3Q-LLM-TE-NLI-Lora16-v1.0 with Docker Model Runner:
docker model run hf.co/T3Q-LLM/T3Q-LLM-TE-NLI-Lora16-v1.0
Model Card for Model ID
Model Details
Model Description
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by: [More Information Needed]
- Funded by [optional]: [More Information Needed]
- Shared by [optional]: [More Information Needed]
- Model type: [More Information Needed]
- Language(s) (NLP): [More Information Needed]
- License: [More Information Needed]
- Finetuned from model [optional]: [More Information Needed]
hf-causal-experimental (pretrained=T3Q-LLM/T3Q-LLM-TE-NLI-Lora16-v1.0,use_accelerate=true,trust_remote_code=true), limit: None, provide_description: False, num_fewshot: 0, batch_size: 8
| Task | Version | Metric | Value | Stderr | |
|---|---|---|---|---|---|
| kobest_boolq | 0 | acc | 0.9573 | ± | 0.0054 |
| macro_f1 | 0.9573 | ± | 0.0054 | ||
| kobest_copa | 0 | acc | 0.7880 | ± | 0.0129 |
| macro_f1 | 0.7877 | ± | 0.0129 | ||
| kobest_hellaswag | 0 | acc | 0.5220 | ± | 0.0224 |
| acc_norm | 0.5440 | ± | 0.0223 | ||
| macro_f1 | 0.5189 | ± | 0.0224 | ||
| kobest_sentineg | 0 | acc | 0.8917 | ± | 0.0156 |
| macro_f1 | 0.8912 | ± | 0.0157 |
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