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Model: BiMediX/BiMediX-Bi Source: Original Platform
2026-06-14 05:16:19 +08:00
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--- a/README.md
+++ b/README.md
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+---
+language:
+- en
+- ar
+metrics:
+- accuracy
+pipeline_tag: text-generation
+tags:
+- medical
+license: cc-by-nc-sa-4.0
+---
+## Model Card for BiMediX-Bilingual
+
+### Model Details
+- **Name:** BiMediX
+- **Version:** 1.0
+- **Type:** Bilingual Medical Mixture of Experts Large Language Model (LLM)
+- **Languages:** English, Arabic
+- **Model Architecture:** [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1)
+- **Training Data:** BiMed1.3M, a bilingual dataset with diverse medical interactions.
+
+### Intended Use
+- **Primary Use:** Medical interactions in both English and Arabic.
+- **Capabilities:** MCQA, closed QA and chats.
+
+## Getting Started
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_id = "BiMediX/BiMediX-Bi"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+text = "Hello BiMediX! I've been experiencing increased tiredness in the past week."
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=500)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+### Training Procedure
+- **Dataset:** BiMed1.3M, 632 million healthcare specialized tokens.
+- **QLoRA Adaptation:** Implements a low-rank adaptation technique, incorporating learnable low-rank adapter weights into the experts and the routing network. This results in training about 4% of the original parameters.
+- **Training Resources:** The model underwent training on approximately 632 million tokens from the Arabic-English corpus, including 288 million tokens exclusively for English.
+
+### Model Performance
+- **Benchmarks:** Outperforms the baseline model and Jais-30B in medical evaluations.
+
+| **Model**                         | **CKG** | **CBio** | **CMed** | **MedGen** | **ProMed** | **Ana** | **MedMCQA** | **MedQA** | **PubmedQA** | **AVG** |
+|-----------------------------------|------------|-----------|-----------|-------------|-------------|---------|-------------|-----------|--------------|---------|
+| Jais-30B | 57.4       | 55.2      | 46.2      | 55.0        | 46.0        | 48.9    | 40.2        | 31.0      | 75.5         | 50.6    |
+| Mixtral-8x7B| 59.1       | 57.6      | 52.6      | 59.5        | 53.3        | 54.4    | 43.2        | 40.6      | 74.7         | 55.0    |
+| **BiMediX (Bilingual)**           | **70.6**   | **72.2**  | **59.3**  | **74.0**    | **64.2**    | **59.6**| **55.8**    | **54.0**  | **78.6**     | **65.4**|
+
+### Safety and Ethical Considerations
+- **Potential issues**: hallucinations, toxicity, stereotypes.
+- **Usage:** Research purposes only.
+
+### Accessibility
+- **Availability:** [BiMediX GitHub Repository](https://github.com/mbzuai-oryx/BiMediX).
+- arxiv.org/abs/2402.13253 
+
+### Authors
+Sara Pieri, Sahal Shaji Mullappilly, Fahad Shahbaz Khan, Rao Muhammad Anwer Salman Khan, Timothy Baldwin, Hisham Cholakkal  
+**Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI)**
--- a/config.json
+++ b/config.json
@@ -0,0 +1,30 @@
+{
+  "_name_or_path": "mistralai/Mixtral-8x7B-v0.1",
+  "architectures": [
+    "MixtralForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 32768,
+  "model_type": "mixtral",
+  "num_attention_heads": 32,
+  "num_experts_per_tok": 2,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "num_local_experts": 8,
+  "output_router_logits": true,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 1000000.0,
+  "router_aux_loss_coef": 0.02,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.37.0",
+  "use_cache": false,
+  "vocab_size": 32000
+}
--- a/generation_config.json
+++ b/generation_config.json
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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "do_sample": true,
+  "eos_token_id": 2,
+  "transformers_version": "4.37.0"
+}
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--- a/special_tokens_map.json
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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "</s>",
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}
--- a/tokenizer.model
+++ b/tokenizer.model
--- a/tokenizer_config.json
+++ b/tokenizer_config.json
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+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+    }
+  },
+  "additional_special_tokens": [],
+  "bos_token": "<s>",
+  "chat_template": "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "legacy": true,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "</s>",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "LlamaTokenizer",
+  "trust_remote_code": true,
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false,
+  "use_fast": true
+}