Text Classification
Transformers
Safetensors
English
llama
text-generation
llama-2
text-embeddings-inference
4-bit precision
awq
Instructions to use TheBloke/llama-2-7B-Guanaco-QLoRA-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TheBloke/llama-2-7B-Guanaco-QLoRA-AWQ with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TheBloke/llama-2-7B-Guanaco-QLoRA-AWQ")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TheBloke/llama-2-7B-Guanaco-QLoRA-AWQ") model = AutoModelForCausalLM.from_pretrained("TheBloke/llama-2-7B-Guanaco-QLoRA-AWQ", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "_name_or_path": "llama2-7b-hf", | |
| "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": "float16", | |
| "transformers_version": "4.32.0.dev0", | |
| "use_cache": true, | |
| "vocab_size": 32000, | |
| "quantization_config": { | |
| "quant_method": "awq", | |
| "zero_point": true, | |
| "group_size": 128, | |
| "bits": 4, | |
| "version": "gemm" | |
| } | |
| } |