Text Classification
Transformers
Safetensors
English
llama
feature-extraction
llama3
reward-model
preference-modeling
rlhf
multi-domain
coherence
commonsense
empathy
multicultural
shared-prompt-gating
custom_code
text-embeddings-inference
Instructions to use mario-rc/multi-domain-rm-fsfairx-llama-3-8b-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mario-rc/multi-domain-rm-fsfairx-llama-3-8b-it with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mario-rc/multi-domain-rm-fsfairx-llama-3-8b-it", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("mario-rc/multi-domain-rm-fsfairx-llama-3-8b-it", trust_remote_code=True) model = AutoModel.from_pretrained("mario-rc/multi-domain-rm-fsfairx-llama-3-8b-it", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,352 Bytes
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"architectures": [
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],
"attention_bias": false,
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"auto_map": {
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"bos_token_id": 128000,
"dtype": "bfloat16",
"eos_token_id": 128001,
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"gating_dropout": 0.05,
"gating_hidden_dim": 64,
"gating_learnable_logit_scale": false,
"gating_logit_scale": 4.0,
"gating_n_hidden": 1,
"gating_temperature": 2.0,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"id2label": {
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"label2id": {
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"max_position_embeddings": 8192,
"mlp_bias": false,
"model_type": "llama",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"num_objectives": 23,
"pad_token_id": 128256,
"pretraining_tp": 1,
"rms_norm_eps": 1e-05,
"rope_parameters": {
"rope_theta": 500000.0,
"rope_type": "default"
},
"shared_prompt_gating": true,
"tie_word_embeddings": false,
"transformers_version": "5.3.0",
"use_cache": false,
"vocab_size": 128257
}
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