Text Classification
Safetensors
MLX
English
neural-txt
bert
reward-model
answer-equivalence
question-answering
Instructions to use paperbd/neuraltxt-reward-tiny-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use paperbd/neuraltxt-reward-tiny-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir neuraltxt-reward-tiny-mlx paperbd/neuraltxt-reward-tiny-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Upload MLX reward tiny model
Browse files- README.md +40 -0
- config.json +35 -0
- model.safetensors +3 -0
- reward_head.safetensors +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +23 -0
README.md
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---
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license: mit
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language: en
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library_name: neural-txt
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pipeline_tag: text-classification
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tags:
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- mlx
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- reward-model
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- answer-equivalence
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- question-answering
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base_model: paperbd/neuraltxt-reward-tiny
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---
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# NeuralTxt Reward Model MLX
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MLX-ready package for [`paperbd/neuraltxt-reward-tiny`](https://huggingface.co/paperbd/neuraltxt-reward-tiny).
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This repo contains:
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- `model.safetensors`: MiniLM/BERT encoder weights readable by MLX.
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- `reward_head.safetensors`: clamped linear reward head in MLX safetensors format.
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- tokenizer files copied from the source reward model.
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## Usage
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```python
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from neuraltxt import NeuralTxtReward
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reward = NeuralTxtReward(backend="mlx")
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score = reward.score(
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response="Paris is the capital of France.",
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reference="The capital of France is Paris.",
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)
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```
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To load this repo explicitly:
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```python
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reward = NeuralTxtReward("paperbd/neuraltxt-reward-tiny-mlx", backend="mlx")
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```
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config.json
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{
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"add_cross_attention": false,
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"architectures": [
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"NeuralTxtRewardMLX"
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],
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"attention_probs_dropout_prob": 0.1,
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"bos_token_id": null,
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"classifier_dropout": null,
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"dtype": "float32",
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"eos_token_id": null,
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 384,
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"initializer_range": 0.02,
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"intermediate_size": 1536,
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"is_decoder": false,
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 6,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"tie_word_embeddings": true,
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"transformers_version": "5.5.0",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 30522,
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"library_name": "neural-txt",
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"base_model": "paperbd/neuraltxt-reward-tiny",
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"reward_head_file": "reward_head.safetensors",
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"model_file": "model.safetensors",
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"reward_pooling": "meanmax"
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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 sha256:3f9156290f370e818ffcc5a3501262fc5e25a1e1e9f3262f64999d5403b8812e
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size 90864176
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reward_head.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:95bd2f1b07413f15f59c3d8fae824a6edda5ec67c43df95cde85395dbabe2511
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size 3238
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tokenizer.json
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tokenizer_config.json
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{
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"backend": "tokenizers",
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"cls_token": "[CLS]",
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"do_basic_tokenize": true,
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"do_lower_case": true,
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"is_local": false,
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"mask_token": "[MASK]",
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"max_length": 128,
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"model_max_length": 512,
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"never_split": null,
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"pad_to_multiple_of": null,
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"pad_token": "[PAD]",
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"pad_token_type_id": 0,
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"padding_side": "right",
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"sep_token": "[SEP]",
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"stride": 0,
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"strip_accents": null,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "BertTokenizer",
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"truncation_side": "right",
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"truncation_strategy": "longest_first",
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"unk_token": "[UNK]"
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}
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