Improve language tag (#1)

- Improve language tag (f62d13f6a9c0c132ca3c61260309e4a398264e0c)


Co-authored-by: Loïck BOURDOIS <[email protected]>

README.md

@@ -1,88 +1,100 @@
----
-license: mit
-language:
-- zh
-base_model:
-- Qwen/Qwen2.5-7B-Instruct
-tags:
-- medical
----
-
-## Model Details
-This model has been LoRA‑fine‑tuned on Qwen2.5‑7B‑Instruct.
-In the future, reinforcement learning training may be carried out based on this model, such as DPRO algorithm, etc.
-
-
-### Base Model Sources [optional]
-
-https://huggingface.co/Qwen/Qwen2.5-7B-Instruct
-
-## How to Get Started with the Model
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-model_name = "ggbaobao/medc_llm_based_on_qwen2.5"
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    device_map="auto",
-    torch_dtype=torch.bfloat16 
-)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-prompt = "猩红热多在发热后多久出现皮疹，请从以下选项中选择：12小时之内, 12～48小时, 60～72小时, 84～96小时, 大于96小时"
-messages = [
-    {"role": "system", "content": "You are Qwen, You are a helpful assistant."},    
-    {"role": "user", "content": prompt},
-]
-text = tokenizer.apply_chat_template(
-    messages,
-    tokenize=False,
-    add_generation_prompt=True
-)
-model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
-
-generated_ids = model.generate(
-    **model_inputs,
-    max_new_tokens=512,
-    do_sample=True
-)  
-generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)]
-response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
-
-print(response)
-```
-## Training Details
-```python
-lora_config = LoraConfig(
-    r=16,              
-    lora_alpha=32,    
-    target_modules=["q_proj", "v_proj"],  
-    lora_dropout=0.1 
-)
-
-training_args = TrainingArguments(
-    output_dir="./results_final1",
-    learning_rate=7e-5,
-    per_device_train_batch_size=2,
-    per_device_eval_batch_size=2,
-    gradient_accumulation_steps=1,  # 梯度累积
-    num_train_epochs=2,
-    evaluation_strategy="steps",
-    # evaluate_steps=1,
-    save_strategy="steps",
-    save_steps=10,
-    logging_steps=10,
-    logging_dir="./logs1",
-    bf16=True,  # 混合精度训练
-```
-### Training Data
-
-The training data comes from https://github.com/SupritYoung/Zhongjing
-If you want to know more details about the above github project, you can also read their paper: 
-Zhongjing: Enhancing the Chinese Medical Capabilities of Large Language Model through Expert Feedback and Real-world Multi-turn Dialogue
-
-The data includes about one-seventh of the multi-round medical consultation data and six-sevenths of the single medical consultation data.
-
-#### Hardware
-vGPU-32GB * 6
-
-#### Software
-use peft and deepspeed
+---
+license: mit
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+base_model:
+- Qwen/Qwen2.5-7B-Instruct
+tags:
+- medical
+---
+
+## Model Details
+This model has been LoRA‑fine‑tuned on Qwen2.5‑7B‑Instruct.
+In the future, reinforcement learning training may be carried out based on this model, such as DPRO algorithm, etc.
+
+
+### Base Model Sources [optional]
+
+https://huggingface.co/Qwen/Qwen2.5-7B-Instruct
+
+## How to Get Started with the Model
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_name = "ggbaobao/medc_llm_based_on_qwen2.5"
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    device_map="auto",
+    torch_dtype=torch.bfloat16 
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+prompt = "猩红热多在发热后多久出现皮疹，请从以下选项中选择：12小时之内, 12～48小时, 60～72小时, 84～96小时, 大于96小时"
+messages = [
+    {"role": "system", "content": "You are Qwen, You are a helpful assistant."},    
+    {"role": "user", "content": prompt},
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512,
+    do_sample=True
+)  
+generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)]
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+
+print(response)
+```
+## Training Details
+```python
+lora_config = LoraConfig(
+    r=16,              
+    lora_alpha=32,    
+    target_modules=["q_proj", "v_proj"],  
+    lora_dropout=0.1 
+)
+
+training_args = TrainingArguments(
+    output_dir="./results_final1",
+    learning_rate=7e-5,
+    per_device_train_batch_size=2,
+    per_device_eval_batch_size=2,
+    gradient_accumulation_steps=1,  # 梯度累积
+    num_train_epochs=2,
+    evaluation_strategy="steps",
+    # evaluate_steps=1,
+    save_strategy="steps",
+    save_steps=10,
+    logging_steps=10,
+    logging_dir="./logs1",
+    bf16=True,  # 混合精度训练
+```
+### Training Data
+
+The training data comes from https://github.com/SupritYoung/Zhongjing
+If you want to know more details about the above github project, you can also read their paper: 
+Zhongjing: Enhancing the Chinese Medical Capabilities of Large Language Model through Expert Feedback and Real-world Multi-turn Dialogue
+
+The data includes about one-seventh of the multi-round medical consultation data and six-sevenths of the single medical consultation data.
+
+#### Hardware
+vGPU-32GB * 6
+
+#### Software
+use peft and deepspeed