Question Answering
Transformers
Safetensors
PEFT
Spanish
llama
text-generation
resident-evil
qlora
llama-cpp
instruct
gaming
text-generation-inference
Instructions to use DavidCaraballoBulnes/ResidentEvil-QA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DavidCaraballoBulnes/ResidentEvil-QA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="DavidCaraballoBulnes/ResidentEvil-QA")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("DavidCaraballoBulnes/ResidentEvil-QA") model = AutoModelForCausalLM.from_pretrained("DavidCaraballoBulnes/ResidentEvil-QA", device_map="auto") - PEFT
How to use DavidCaraballoBulnes/ResidentEvil-QA with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
File size: 760 Bytes
81ea5a2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | {
"architectures": [
"LlamaForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 1,
"dtype": "bfloat16",
"eos_token_id": 2,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 11008,
"max_position_embeddings": 8192,
"mlp_bias": false,
"model_type": "llama",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"pad_token_id": null,
"pretraining_tp": 1,
"rms_norm_eps": 1e-05,
"rope_parameters": {
"rope_theta": 10000.0,
"rope_type": "default"
},
"tie_word_embeddings": false,
"transformers_version": "5.3.0",
"use_cache": true,
"vocab_size": 256000
}
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