Question Answering
Transformers
PyTorch
English
llama
text-generation
language-agent
reasoning
grounding
text-generation-inference
Instructions to use ai2lumos/lumos_complex_qa_plan_onetime with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ai2lumos/lumos_complex_qa_plan_onetime with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="ai2lumos/lumos_complex_qa_plan_onetime")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ai2lumos/lumos_complex_qa_plan_onetime") model = AutoModelForCausalLM.from_pretrained("ai2lumos/lumos_complex_qa_plan_onetime", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "/net/nfs/mosaic/day/llama_hf/llama-2-7b", | |
| "architectures": [ | |
| "LlamaForCausalLM" | |
| ], | |
| "bos_token_id": 1, | |
| "eos_token_id": 2, | |
| "hidden_act": "silu", | |
| "hidden_size": 4096, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 11008, | |
| "max_position_embeddings": 2048, | |
| "model_type": "llama", | |
| "num_attention_heads": 32, | |
| "num_hidden_layers": 32, | |
| "num_key_value_heads": 32, | |
| "pad_token_id": 0, | |
| "pretraining_tp": 1, | |
| "rms_norm_eps": 1e-05, | |
| "rope_scaling": null, | |
| "tie_word_embeddings": false, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.32.0.dev0", | |
| "use_cache": true, | |
| "vocab_size": 32001 | |
| } | |