Instructions to use trl-internal-testing/tiny-Qwen2ForSequenceClassification-2.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use trl-internal-testing/tiny-Qwen2ForSequenceClassification-2.5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="trl-internal-testing/tiny-Qwen2ForSequenceClassification-2.5")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("trl-internal-testing/tiny-Qwen2ForSequenceClassification-2.5") model = AutoModelForSequenceClassification.from_pretrained("trl-internal-testing/tiny-Qwen2ForSequenceClassification-2.5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload Qwen2ForSequenceClassification
#2
by albertvillanova HF Staff - opened
- config.json +2 -1
- generation_config.json +1 -1
- model.safetensors +2 -2
config.json
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"bos_token_id": 151643,
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"dtype": "bfloat16",
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"eos_token_id": 151645,
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"hidden_act": "silu",
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"hidden_size": 16,
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"id2label": {
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"rope_theta": 1000000.0,
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"sliding_window": null,
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"tie_word_embeddings": false,
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"transformers_version": "4.
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 152064
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"bos_token_id": 151643,
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"dtype": "bfloat16",
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"eos_token_id": 151645,
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"head_dim": 8,
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"hidden_act": "silu",
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"hidden_size": 16,
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"id2label": {
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"rope_theta": 1000000.0,
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"sliding_window": null,
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"tie_word_embeddings": false,
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"transformers_version": "4.56.2",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 152064
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generation_config.json
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"temperature": 0.7,
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"top_k": 20,
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"top_p": 0.8,
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"transformers_version": "4.
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}
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"temperature": 0.7,
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"top_k": 20,
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"top_p": 0.8,
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"transformers_version": "4.56.2"
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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size 4881600
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