Text Classification
Transformers
Safetensors
mistral
Generated from Trainer
trl
reward-trainer
text-embeddings-inference
Instructions to use rlhf-and-friends/TLDR-Mistral-7B-RM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rlhf-and-friends/TLDR-Mistral-7B-RM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rlhf-and-friends/TLDR-Mistral-7B-RM")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rlhf-and-friends/TLDR-Mistral-7B-RM") model = AutoModelForSequenceClassification.from_pretrained("rlhf-and-friends/TLDR-Mistral-7B-RM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 788 Bytes
80cce59 | 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 34 35 | {
"_name_or_path": "mistral-community/Mistral-7B-v0.2",
"architectures": [
"MistralForSequenceClassification"
],
"attention_dropout": 0.0,
"bos_token_id": 1,
"eos_token_id": 2,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"id2label": {
"0": "LABEL_0"
},
"initializer_range": 0.02,
"intermediate_size": 14336,
"label2id": {
"LABEL_0": 0
},
"max_position_embeddings": 32768,
"model_type": "mistral",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"pad_token_id": 32000,
"rms_norm_eps": 1e-05,
"rope_theta": 1000000.0,
"sliding_window": null,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.47.1",
"use_cache": true,
"vocab_size": 32000
}
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