Improve language tag

Hi! As the model is multilingual, this is a PR to add other languages than English to the language tag to improve the referencing. Note that 29 languages are announced in the README, but only 13 are explicitly listed. I was therefore only able to add these 13 languages.

README.md

@@ -1,116 +1,128 @@
----
-library_name: transformers
-license: apache-2.0
-license_link: https://huggingface.co/huihui-ai/Qwen2.5-7B-Instruct-abliterated-v3/blob/main/LICENSE
-language:
-- en
-pipeline_tag: text-generation
-base_model: Qwen/Qwen2.5-7B-Instruct
-tags:
-- chat
-- abliterated
-- uncensored
----
-
-# huihui-ai/Qwen2.5-7B-Instruct-abliterated-v3
-
-
-This is an uncensored version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) created with abliteration (see [remove-refusals-with-transformers](https://github.com/Sumandora/remove-refusals-with-transformers) to know more about it).
-This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. 
-The test results are not very good, but compared to before, there is much less [garbled text](https://huggingface.co/huihui-ai/Qwen2.5-7B-Instruct-abliterated-v2/discussions/2).
-
-## ollama
-
-You can use [huihui_ai/qwen2.5-abliterate](https://ollama.com/huihui_ai/qwen2.5-abliterate) directly, 
-```
-ollama run huihui_ai/qwen2.5-abliterate
-```
-
-## Usage
-You can use this model in your applications by loading it with Hugging Face's `transformers` library:
-
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-# Load the model and tokenizer
-model_name = "huihui-ai/Qwen2.5-7B-Instruct-abliterated-v3"
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    torch_dtype="auto",
-    device_map="auto"
-)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-# Initialize conversation context
-initial_messages = [
-    {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}
-]
-messages = initial_messages.copy()  # Copy the initial conversation context
-
-# Enter conversation loop
-while True:
-    # Get user input
-    user_input = input("User: ").strip()  # Strip leading and trailing spaces
-
-    # If the user types '/exit', end the conversation
-    if user_input.lower() == "/exit":
-        print("Exiting chat.")
-        break
-
-    # If the user types '/clean', reset the conversation context
-    if user_input.lower() == "/clean":
-        messages = initial_messages.copy()  # Reset conversation context
-        print("Chat history cleared. Starting a new conversation.")
-        continue
-
-    # If input is empty, prompt the user and continue
-    if not user_input:
-        print("Input cannot be empty. Please enter something.")
-        continue
-
-    # Add user input to the conversation
-    messages.append({"role": "user", "content": user_input})
-
-    # Build the chat template
-    text = tokenizer.apply_chat_template(
-        messages,
-        tokenize=False,
-        add_generation_prompt=True
-    )
-
-    # Tokenize input and prepare it for the model
-    model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
-
-    # Generate a response from the model
-    generated_ids = model.generate(
-        **model_inputs,
-        max_new_tokens=8192
-    )
-
-    # Extract model output, removing special tokens
-    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]
-
-    # Add the model's response to the conversation
-    messages.append({"role": "assistant", "content": response})
-
-    # Print the model's response
-    print(f"Qwen: {response}")
-
-```
-
-## Evaluations
-The following data has been re-evaluated and calculated as the average for each test.
-
-| Benchmark   | Qwen2.5-7B-Instruct | Qwen2.5-7B-Instruct-abliterated-v3 | Qwen2.5-7B-Instruct-abliterated-v2 | Qwen2.5-7B-Instruct-abliterated |
-|-------------|---------------------|------------------------------------|------------------------------------|---------------------------------|
-| IF_Eval     | 76.44               | 72.64                              | **77.82**                          | 76.49                           |
-| MMLU Pro    | **43.12**           | 39.14                              | 42.03                              | 41.71                           |
-| TruthfulQA  | 62.46               | 57.27                              | 57.81                              | **64.92**                       |
-| BBH         | **53.92**           | 50.67                              | 53.01                              | 52.77                           |
-| GPQA        | 31.91               | 31.65                              | **32.17**                          | 31.97                           |
-
-The script used for evaluation can be found inside this repository under /eval.sh, or click [here](https://huggingface.co/huihui-ai/Qwen2.5-7B-Instruct-abliterated-v3/blob/main/eval.sh)
+---
+library_name: transformers
+license: apache-2.0
+license_link: https://huggingface.co/huihui-ai/Qwen2.5-7B-Instruct-abliterated-v3/blob/main/LICENSE
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+pipeline_tag: text-generation
+base_model: Qwen/Qwen2.5-7B-Instruct
+tags:
+- chat
+- abliterated
+- uncensored
+---
+
+# huihui-ai/Qwen2.5-7B-Instruct-abliterated-v3
+
+
+This is an uncensored version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) created with abliteration (see [remove-refusals-with-transformers](https://github.com/Sumandora/remove-refusals-with-transformers) to know more about it).
+This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. 
+The test results are not very good, but compared to before, there is much less [garbled text](https://huggingface.co/huihui-ai/Qwen2.5-7B-Instruct-abliterated-v2/discussions/2).
+
+## ollama
+
+You can use [huihui_ai/qwen2.5-abliterate](https://ollama.com/huihui_ai/qwen2.5-abliterate) directly, 
+```
+ollama run huihui_ai/qwen2.5-abliterate
+```
+
+## Usage
+You can use this model in your applications by loading it with Hugging Face's `transformers` library:
+
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load the model and tokenizer
+model_name = "huihui-ai/Qwen2.5-7B-Instruct-abliterated-v3"
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Initialize conversation context
+initial_messages = [
+    {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}
+]
+messages = initial_messages.copy()  # Copy the initial conversation context
+
+# Enter conversation loop
+while True:
+    # Get user input
+    user_input = input("User: ").strip()  # Strip leading and trailing spaces
+
+    # If the user types '/exit', end the conversation
+    if user_input.lower() == "/exit":
+        print("Exiting chat.")
+        break
+
+    # If the user types '/clean', reset the conversation context
+    if user_input.lower() == "/clean":
+        messages = initial_messages.copy()  # Reset conversation context
+        print("Chat history cleared. Starting a new conversation.")
+        continue
+
+    # If input is empty, prompt the user and continue
+    if not user_input:
+        print("Input cannot be empty. Please enter something.")
+        continue
+
+    # Add user input to the conversation
+    messages.append({"role": "user", "content": user_input})
+
+    # Build the chat template
+    text = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True
+    )
+
+    # Tokenize input and prepare it for the model
+    model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+    # Generate a response from the model
+    generated_ids = model.generate(
+        **model_inputs,
+        max_new_tokens=8192
+    )
+
+    # Extract model output, removing special tokens
+    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]
+
+    # Add the model's response to the conversation
+    messages.append({"role": "assistant", "content": response})
+
+    # Print the model's response
+    print(f"Qwen: {response}")
+
+```
+
+## Evaluations
+The following data has been re-evaluated and calculated as the average for each test.
+
+| Benchmark   | Qwen2.5-7B-Instruct | Qwen2.5-7B-Instruct-abliterated-v3 | Qwen2.5-7B-Instruct-abliterated-v2 | Qwen2.5-7B-Instruct-abliterated |
+|-------------|---------------------|------------------------------------|------------------------------------|---------------------------------|
+| IF_Eval     | 76.44               | 72.64                              | **77.82**                          | 76.49                           |
+| MMLU Pro    | **43.12**           | 39.14                              | 42.03                              | 41.71                           |
+| TruthfulQA  | 62.46               | 57.27                              | 57.81                              | **64.92**                       |
+| BBH         | **53.92**           | 50.67                              | 53.01                              | 52.77                           |
+| GPQA        | 31.91               | 31.65                              | **32.17**                          | 31.97                           |
+
+The script used for evaluation can be found inside this repository under /eval.sh, or click [here](https://huggingface.co/huihui-ai/Qwen2.5-7B-Instruct-abliterated-v3/blob/main/eval.sh)