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

@@ -1,138 +1,150 @@
----
-base_model: Qwen/Qwen2.5-14B-Instruct
-tags:
-- text-generation-inference
-- transformers
-- unsloth
-- qwen2
-- trl
-- gammacorpus
-- zurich
-- chat
-- conversational
-license: apache-2.0
-language:
-- en
-datasets:
-- rubenroy/GammaCorpus-v2-50k
-pipeline_tag: text-generation
-library_name: transformers
----
-
-![Zunich Banner](https://cdn.ruben-roy.com/AI/Zurich/img/banner-14B-50k.png)
-
-# Zurich 14B GammaCorpus v2-50k
-*A Qwen 2.5 model fine-tuned on the GammaCorpus dataset*
-
-## Overview
-Zurich 14B GammaCorpus v2-50k is a fine-tune of Alibaba's **Qwen 2.5 14B Instruct** model. Zurich is designed to outperform other models that have a similar size while also showcasing [GammaCorpus v2-50k](https://huggingface.co/datasets/rubenroy/GammaCorpus-v2-50k).
-
-## Model Details
-- **Base Model:** [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct)
-- **Type:** Causal Language Models
-- **Architecture:** Transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
-- **Number of Parameters:** 14.7B
-- **Number of Paramaters (Non-Embedding):** 13.1B
-- **Number of Layers:** 48
-- **Number of Attention Heads (GQA):** 40 for Q and 8 for KV
-
-## Training Details
-
-Zurich-14B-GCv2-50k underwent fine-tuning with 1 A100 GPU for ~20 minutes and trained with the [Unsloth](https://unsloth.ai/) framework. Zurich-14B-GCv2-50k was trained for **60 Epochs**. 
-
-## Usage
-
-### Requirements
-
-We **strongly** recommend you use the latest version of the `transformers` package. You may install it via `pip` as follows:
-
-```
-pip install transformers
-```
-
-### Quickstart
-
-Here is a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents;
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model_name = "rubenroy/Zurich-14B-GCv2-50k"
-
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    torch_dtype="auto",
-    device_map="auto"
-)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-prompt = "How tall is the Eiffel tower?"
-messages = [
-    {"role": "system", "content": "You are Zurich, an AI assistant built on the Qwen 2.5 14B model developed by Alibaba Cloud, and fine-tuned by Ruben Roy. 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
-)
-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]
-```
-
-## About GammaCorpus
-
-This model, and all Zurich models, are trained with GammaCorpus. GammaCorpus is a dataset on HuggingFace that is filled with structured and filtered multi-turn conversations.
-GammaCorpus has 4 version with different sizes in each. These are the following versions and sizes:
-
-### GammaCorpus v1
-- 10k UNFILTERED
-- 50k UNFILTERED
-- 70k UNFILTERED
-
-Here is a link to the GCv1 dataset collection:<br>
-https://huggingface.co/collections/rubenroy/gammacorpus-v1-67935e4e52a04215f15a7a60
-
-### GammaCorpus v2
-- 10k
-- **50k <-- This is the version of GammaCorpus v2 that the Zurich model you are using was trained on.**
-- 100k
-- 500k
-- 1m
-- 5m
-
-Here is a link to the GCv2 dataset collection:<br>
-https://huggingface.co/collections/rubenroy/gammacorpus-v2-67935e895e1259c404a579df
-
-### GammaCorpus CoT
-- Math 170k
-
-Here is a link to the GC-CoT dataset collection:<br>
-https://huggingface.co/collections/rubenroy/gammacorpus-cot-6795bbc950b62b1ced41d14f
-
-### GammaCorpus QA
-- Fact 450k
-
-Here is a link to the GC-QA dataset collection:<br>
-https://huggingface.co/collections/rubenroy/gammacorpus-qa-679857017bb3855234c1d8c7
-
-### The link to the full GammaCorpus dataset collection can be found [here](https://huggingface.co/collections/rubenroy/gammacorpus-67765abf607615a0eb6d61ac).
-
-## Known Limitations
-
-- **Bias:** We have tried our best to mitigate as much bias we can, but please be aware of the possibility that the model might generate some biased answers.
-
-## Additional Information
-
-### Licensing Information
-
+---
+base_model: Qwen/Qwen2.5-14B-Instruct
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- qwen2
+- trl
+- gammacorpus
+- zurich
+- chat
+- conversational
+license: apache-2.0
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+datasets:
+- rubenroy/GammaCorpus-v2-50k
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+![Zunich Banner](https://cdn.ruben-roy.com/AI/Zurich/img/banner-14B-50k.png)
+
+# Zurich 14B GammaCorpus v2-50k
+*A Qwen 2.5 model fine-tuned on the GammaCorpus dataset*
+
+## Overview
+Zurich 14B GammaCorpus v2-50k is a fine-tune of Alibaba's **Qwen 2.5 14B Instruct** model. Zurich is designed to outperform other models that have a similar size while also showcasing [GammaCorpus v2-50k](https://huggingface.co/datasets/rubenroy/GammaCorpus-v2-50k).
+
+## Model Details
+- **Base Model:** [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct)
+- **Type:** Causal Language Models
+- **Architecture:** Transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
+- **Number of Parameters:** 14.7B
+- **Number of Paramaters (Non-Embedding):** 13.1B
+- **Number of Layers:** 48
+- **Number of Attention Heads (GQA):** 40 for Q and 8 for KV
+
+## Training Details
+
+Zurich-14B-GCv2-50k underwent fine-tuning with 1 A100 GPU for ~20 minutes and trained with the [Unsloth](https://unsloth.ai/) framework. Zurich-14B-GCv2-50k was trained for **60 Epochs**. 
+
+## Usage
+
+### Requirements
+
+We **strongly** recommend you use the latest version of the `transformers` package. You may install it via `pip` as follows:
+
+```
+pip install transformers
+```
+
+### Quickstart
+
+Here is a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents;
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "rubenroy/Zurich-14B-GCv2-50k"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "How tall is the Eiffel tower?"
+messages = [
+    {"role": "system", "content": "You are Zurich, an AI assistant built on the Qwen 2.5 14B model developed by Alibaba Cloud, and fine-tuned by Ruben Roy. 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
+)
+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]
+```
+
+## About GammaCorpus
+
+This model, and all Zurich models, are trained with GammaCorpus. GammaCorpus is a dataset on HuggingFace that is filled with structured and filtered multi-turn conversations.
+GammaCorpus has 4 version with different sizes in each. These are the following versions and sizes:
+
+### GammaCorpus v1
+- 10k UNFILTERED
+- 50k UNFILTERED
+- 70k UNFILTERED
+
+Here is a link to the GCv1 dataset collection:<br>
+https://huggingface.co/collections/rubenroy/gammacorpus-v1-67935e4e52a04215f15a7a60
+
+### GammaCorpus v2
+- 10k
+- **50k <-- This is the version of GammaCorpus v2 that the Zurich model you are using was trained on.**
+- 100k
+- 500k
+- 1m
+- 5m
+
+Here is a link to the GCv2 dataset collection:<br>
+https://huggingface.co/collections/rubenroy/gammacorpus-v2-67935e895e1259c404a579df
+
+### GammaCorpus CoT
+- Math 170k
+
+Here is a link to the GC-CoT dataset collection:<br>
+https://huggingface.co/collections/rubenroy/gammacorpus-cot-6795bbc950b62b1ced41d14f
+
+### GammaCorpus QA
+- Fact 450k
+
+Here is a link to the GC-QA dataset collection:<br>
+https://huggingface.co/collections/rubenroy/gammacorpus-qa-679857017bb3855234c1d8c7
+
+### The link to the full GammaCorpus dataset collection can be found [here](https://huggingface.co/collections/rubenroy/gammacorpus-67765abf607615a0eb6d61ac).
+
+## Known Limitations
+
+- **Bias:** We have tried our best to mitigate as much bias we can, but please be aware of the possibility that the model might generate some biased answers.
+
+## Additional Information
+
+### Licensing Information
+
 The model is released under the **[Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0)**. Please refer to the license for usage rights and restrictions.