Improve language tag

README.md

@@ -1,60 +1,72 @@
----
-license: apache-2.0
-language:
-- en
-base_model:
-- Qwen/Qwen2.5-1.5B-Instruct
-pipeline_tag: text-generation
-library_name: transformers
-tags:
-- trl
-- Reinforcement learning
----
-# **Bellatrix-Tiny-1.5B-R1**
-
-Bellatrix is based on a reasoning-based model designed for the DeepSeek-R1 synthetic dataset entries. The pipeline's instruction-tuned, text-only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. These models outperform many of the available open-source options. Bellatrix is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions utilize supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF).
-
-# **Use with transformers**
-
-Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function.
-
-Make sure to update your transformers installation via `pip install --upgrade transformers`.
-
-```python
-import torch
-from transformers import pipeline
-
-model_id = "prithivMLmods/Bellatrix-Tiny-1.5B-R1"
-pipe = pipeline(
-    "text-generation",
-    model=model_id,
-    torch_dtype=torch.bfloat16,
-    device_map="auto",
-)
-messages = [
-    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
-    {"role": "user", "content": "Who are you?"},
-]
-outputs = pipe(
-    messages,
-    max_new_tokens=256,
-)
-print(outputs[0]["generated_text"][-1])
-```
-
-Note: You can also find detailed recipes on how to use the model locally, with `torch.compile()`, assisted generations, quantized and more at [`huggingface-llama-recipes`](https://github.com/huggingface/huggingface-llama-recipes)
-
-# **Intended Use**  
-Bellatrix is designed for applications that require advanced reasoning and multilingual dialogue capabilities. It is particularly suitable for:  
-- **Agentic Retrieval**: Enabling intelligent retrieval of relevant information in a dialogue or query-response system.  
-- **Summarization Tasks**: Condensing large bodies of text into concise summaries for easier comprehension.  
-- **Multilingual Use Cases**: Supporting conversations in multiple languages with high accuracy and coherence.  
-- **Instruction-Based Applications**: Following complex, context-aware instructions to generate precise outputs in a variety of scenarios.
-
-# **Limitations**  
-Despite its capabilities, Bellatrix has some limitations:  
-1. **Domain Specificity**: While it performs well on general tasks, its performance may degrade with highly specialized or niche datasets.  
-2. **Dependence on Training Data**: It is only as good as the quality and diversity of its training data, which may lead to biases or inaccuracies.  
-3. **Computational Resources**: The model’s optimized transformer architecture can be resource-intensive, requiring significant computational power for fine-tuning and inference.  
-4. **Language Coverage**: While multilingual, some languages or dialects may have limited support or lower performance compared to widely used ones.  
+---
+license: apache-2.0
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+base_model:
+- Qwen/Qwen2.5-1.5B-Instruct
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- trl
+- Reinforcement learning
+---
+# **Bellatrix-Tiny-1.5B-R1**
+
+Bellatrix is based on a reasoning-based model designed for the DeepSeek-R1 synthetic dataset entries. The pipeline's instruction-tuned, text-only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. These models outperform many of the available open-source options. Bellatrix is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions utilize supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF).
+
+# **Use with transformers**
+
+Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function.
+
+Make sure to update your transformers installation via `pip install --upgrade transformers`.
+
+```python
+import torch
+from transformers import pipeline
+
+model_id = "prithivMLmods/Bellatrix-Tiny-1.5B-R1"
+pipe = pipeline(
+    "text-generation",
+    model=model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+messages = [
+    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
+    {"role": "user", "content": "Who are you?"},
+]
+outputs = pipe(
+    messages,
+    max_new_tokens=256,
+)
+print(outputs[0]["generated_text"][-1])
+```
+
+Note: You can also find detailed recipes on how to use the model locally, with `torch.compile()`, assisted generations, quantized and more at [`huggingface-llama-recipes`](https://github.com/huggingface/huggingface-llama-recipes)
+
+# **Intended Use**  
+Bellatrix is designed for applications that require advanced reasoning and multilingual dialogue capabilities. It is particularly suitable for:  
+- **Agentic Retrieval**: Enabling intelligent retrieval of relevant information in a dialogue or query-response system.  
+- **Summarization Tasks**: Condensing large bodies of text into concise summaries for easier comprehension.  
+- **Multilingual Use Cases**: Supporting conversations in multiple languages with high accuracy and coherence.  
+- **Instruction-Based Applications**: Following complex, context-aware instructions to generate precise outputs in a variety of scenarios.
+
+# **Limitations**  
+Despite its capabilities, Bellatrix has some limitations:  
+1. **Domain Specificity**: While it performs well on general tasks, its performance may degrade with highly specialized or niche datasets.  
+2. **Dependence on Training Data**: It is only as good as the quality and diversity of its training data, which may lead to biases or inaccuracies.  
+3. **Computational Resources**: The model’s optimized transformer architecture can be resource-intensive, requiring significant computational power for fine-tuning and inference.  
+4. **Language Coverage**: While multilingual, some languages or dialects may have limited support or lower performance compared to widely used ones.  
 5. **Real-World Contexts**: It may struggle with understanding nuanced or ambiguous real-world scenarios not covered during training.