Import from ik-ram28/MedLLaMA-13B-CPT + standardized README
Browse filesThis view is limited to 50 files because it contains too many changes.
See raw diff
- README.md +22 -0
- added_tokens.json +3 -0
- config.json +31 -0
- generation_config.json +7 -0
- model-00001-of-00011.safetensors +3 -0
- model-00002-of-00011.safetensors +3 -0
- model-00003-of-00011.safetensors +3 -0
- model-00004-of-00011.safetensors +3 -0
- model-00005-of-00011.safetensors +3 -0
- model-00006-of-00011.safetensors +3 -0
- model-00007-of-00011.safetensors +3 -0
- model-00008-of-00011.safetensors +3 -0
- model-00009-of-00011.safetensors +3 -0
- model-00010-of-00011.safetensors +3 -0
- model-00011-of-00011.safetensors +3 -0
- model.safetensors.index.json +371 -0
- rng_state_0.pth +3 -0
- rng_state_1.pth +3 -0
- rng_state_10.pth +3 -0
- rng_state_11.pth +3 -0
- rng_state_12.pth +3 -0
- rng_state_13.pth +3 -0
- rng_state_14.pth +3 -0
- rng_state_15.pth +3 -0
- rng_state_16.pth +3 -0
- rng_state_17.pth +3 -0
- rng_state_18.pth +3 -0
- rng_state_19.pth +3 -0
- rng_state_2.pth +3 -0
- rng_state_20.pth +3 -0
- rng_state_21.pth +3 -0
- rng_state_22.pth +3 -0
- rng_state_23.pth +3 -0
- rng_state_24.pth +3 -0
- rng_state_25.pth +3 -0
- rng_state_26.pth +3 -0
- rng_state_27.pth +3 -0
- rng_state_28.pth +3 -0
- rng_state_29.pth +3 -0
- rng_state_3.pth +3 -0
- rng_state_30.pth +3 -0
- rng_state_31.pth +3 -0
- rng_state_4.pth +3 -0
- rng_state_5.pth +3 -0
- rng_state_6.pth +3 -0
- rng_state_7.pth +3 -0
- rng_state_8.pth +3 -0
- rng_state_9.pth +3 -0
- scheduler.pt +3 -0
- special_tokens_map.json +30 -0
README.md
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---
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language:
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- fr
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- en
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license: apache-2.0
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tags:
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- medical
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- domain-adaptation
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- continual-pretraining
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- causal-lm
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- question-answering
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- evaluation
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datasets:
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- Dr-BERT/NACHOS
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base_model: MedLLaMA-13B
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model_type: causal-lm
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---
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# MedLLaMA-13B-CPT (CPT)
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## Model description
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This checkpoint is a **continual-pretrained (CPT)** version of **MedLLaMA-13B**, adapted on unlabeled french medical text to strengthen domain-specific representations for medical question answering. CPT is performed via **full-parameter training** on medical corpora (Dr-BERT/NACHOS).
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added_tokens.json
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{
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"<pad>": 32000
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}
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 0,
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"eos_token_id": 1,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 5120,
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"initializer_range": 0.02,
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"intermediate_size": 13824,
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"max_position_embeddings": 2048,
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"max_sequence_length": 2048,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 40,
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"num_hidden_layers": 40,
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"num_key_value_heads": 40,
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"pad_token_id": -1,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta": 10000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "float32",
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"transformers_version": "4.53.2",
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"use_cache": false,
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"vocab_size": 32000
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}
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generation_config.json
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"transformers_version": "4.53.2"
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}
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model-00001-of-00011.safetensors
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