Improve language tag

README.md

@@ -1,229 +1,235 @@
----
-license: apache-2.0
-language:
-- en
-library_name: transformers
-base_model:
-- Qwen/Qwen2.5-1.5B-Instruct
-pipeline_tag: text-generation
-model-index:
-- name: Bellatrix-1.5B-xElite
-  results:
-  - task:
-      type: text-generation
-      name: Text Generation
-    dataset:
-      name: IFEval (0-Shot)
-      type: wis-k/instruction-following-eval
-      split: train
-      args:
-        num_few_shot: 0
-    metrics:
-    - type: inst_level_strict_acc and prompt_level_strict_acc
-      value: 19.64
-      name: averaged accuracy
-    source:
-      url: >-
-        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FBellatrix-1.5B-xElite
-      name: Open LLM Leaderboard
-  - task:
-      type: text-generation
-      name: Text Generation
-    dataset:
-      name: BBH (3-Shot)
-      type: SaylorTwift/bbh
-      split: test
-      args:
-        num_few_shot: 3
-    metrics:
-    - type: acc_norm
-      value: 9.49
-      name: normalized accuracy
-    source:
-      url: >-
-        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FBellatrix-1.5B-xElite
-      name: Open LLM Leaderboard
-  - task:
-      type: text-generation
-      name: Text Generation
-    dataset:
-      name: MATH Lvl 5 (4-Shot)
-      type: lighteval/MATH-Hard
-      split: test
-      args:
-        num_few_shot: 4
-    metrics:
-    - type: exact_match
-      value: 12.61
-      name: exact match
-    source:
-      url: >-
-        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FBellatrix-1.5B-xElite
-      name: Open LLM Leaderboard
-  - task:
-      type: text-generation
-      name: Text Generation
-    dataset:
-      name: GPQA (0-shot)
-      type: Idavidrein/gpqa
-      split: train
-      args:
-        num_few_shot: 0
-    metrics:
-    - type: acc_norm
-      value: 3.8
-      name: acc_norm
-    source:
-      url: >-
-        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FBellatrix-1.5B-xElite
-      name: Open LLM Leaderboard
-  - task:
-      type: text-generation
-      name: Text Generation
-    dataset:
-      name: MuSR (0-shot)
-      type: TAUR-Lab/MuSR
-      args:
-        num_few_shot: 0
-    metrics:
-    - type: acc_norm
-      value: 4.44
-      name: acc_norm
-    source:
-      url: >-
-        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FBellatrix-1.5B-xElite
-      name: Open LLM Leaderboard
-  - task:
-      type: text-generation
-      name: Text Generation
-    dataset:
-      name: MMLU-PRO (5-shot)
-      type: TIGER-Lab/MMLU-Pro
-      config: main
-      split: test
-      args:
-        num_few_shot: 5
-    metrics:
-    - type: acc
-      value: 7.3
-      name: accuracy
-    source:
-      url: >-
-        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FBellatrix-1.5B-xElite
-      name: Open LLM Leaderboard
-tags:
-- qwen
-- qwq
----
-<pre align="center">
- ____  ____  __    __      __   ____  ____  ____  _  _ 
-(  _ \( ___)(  )  (  )    /__\ (_  _)(  _ \(_  _)( \/ )
- ) _ < )__)  )(__  )(__  /(__)\  )(   )   / _)(_  )  ( 
-(____/(____)(____)(____)(__)(__)(__) (_)\_)(____)(_/\_)
-</pre>
-
-# **Bellatrix-1.5B-xElite**
-
-Bellatrix-1.5B-xElite is based on a reasoning-based model designed for the QWQ 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).
-
-# **Quickstart with Transformers**
-
-Here provides 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 = "prithivMLmods/Bellatrix-1.5B-xElite"
-
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    torch_dtype="auto",
-    device_map="auto"
-)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-prompt = "Give me a short introduction to large language model."
-messages = [
-    {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. 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]
-```
-
-# **Intended Use:**
-
-1. **Multilingual Dialogue Systems:**
-   - Designed for conversational AI applications, capable of handling dialogue across multiple languages.
-   - Useful in customer service, chatbots, and other dialogue-centric use cases.
-
-2. **Reasoning and QWQ Dataset Applications:**
-   - Optimized for tasks requiring logical reasoning and contextual understanding, particularly in synthetic datasets like QWQ.
-
-3. **Agentic Retrieval:**
-   - Supports retrieval-augmented generation tasks, helping systems fetch and synthesize information effectively.
-
-4. **Summarization Tasks:**
-   - Excels in summarizing long or complex text while maintaining coherence and relevance.
-
-5. **Instruction-Following Tasks:**
-   - Can execute tasks based on specific user instructions due to instruction-tuning during training.
-
-6. **Language Generation:**
-   - Suitable for generating coherent and contextually relevant text in various domains and styles.
-
-# **Limitations:**
-
-1. **Synthetic Dataset Bias:**
-   - Optimization for QWQ and similar datasets may make the model less effective on real-world or less structured data.
-
-2. **Data Dependency:**
-   - Performance may degrade on tasks or languages not well-represented in the training dataset.
-
-3. **Computational Requirements:**
-   - The optimized transformer architecture may demand significant computational resources, especially for fine-tuning or large-scale deployments.
-
-4. **Potential Hallucinations:**
-   - Like most auto-regressive models, it may generate plausible-sounding but factually incorrect or nonsensical outputs.
-
-5. **RLHF-Specific Biases:**
-   - Reinforcement Learning with Human Feedback (RLHF) can introduce biases based on the preferences of the annotators involved in the feedback process.
-
-6. **Limited Domain Adaptability:**
-   - While effective in reasoning and dialogue tasks, it may struggle with highly specialized domains or out-of-distribution tasks.
-
-7. **Multilingual Limitations:**
-   - Although optimized for multilingual use, certain low-resource languages may exhibit poorer performance compared to high-resource ones.
-
-8. **Ethical Concerns:**
-   - May inadvertently generate inappropriate or harmful content if safeguards are not applied, particularly in sensitive applications.
-
-9. **Real-Time Usability:**
-   - Latency in inference time could limit its effectiveness in real-time applications or when scaling to large user bases.
-# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
-Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/prithivMLmods__Bellatrix-1.5B-xElite-details)!
-Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=prithivMLmods%2FBellatrix-1.5B-xElite&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)!
-
-|      Metric       |Value (%)|
-|-------------------|--------:|
-|**Average**        |     9.55|
-|IFEval (0-Shot)    |    19.64|
-|BBH (3-Shot)       |     9.49|
-|MATH Lvl 5 (4-Shot)|    12.61|
-|GPQA (0-shot)      |     3.80|
-|MuSR (0-shot)      |     4.44|
+---
+license: apache-2.0
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+library_name: transformers
+base_model:
+- Qwen/Qwen2.5-1.5B-Instruct
+pipeline_tag: text-generation
+tags:
+- qwen
+- qwq
+model-index:
+- name: Bellatrix-1.5B-xElite
+  results:
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: IFEval (0-Shot)
+      type: wis-k/instruction-following-eval
+      split: train
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: inst_level_strict_acc and prompt_level_strict_acc
+      value: 19.64
+      name: averaged accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FBellatrix-1.5B-xElite
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: BBH (3-Shot)
+      type: SaylorTwift/bbh
+      split: test
+      args:
+        num_few_shot: 3
+    metrics:
+    - type: acc_norm
+      value: 9.49
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FBellatrix-1.5B-xElite
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MATH Lvl 5 (4-Shot)
+      type: lighteval/MATH-Hard
+      split: test
+      args:
+        num_few_shot: 4
+    metrics:
+    - type: exact_match
+      value: 12.61
+      name: exact match
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FBellatrix-1.5B-xElite
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: GPQA (0-shot)
+      type: Idavidrein/gpqa
+      split: train
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: acc_norm
+      value: 3.8
+      name: acc_norm
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FBellatrix-1.5B-xElite
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MuSR (0-shot)
+      type: TAUR-Lab/MuSR
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: acc_norm
+      value: 4.44
+      name: acc_norm
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FBellatrix-1.5B-xElite
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MMLU-PRO (5-shot)
+      type: TIGER-Lab/MMLU-Pro
+      config: main
+      split: test
+      args:
+        num_few_shot: 5
+    metrics:
+    - type: acc
+      value: 7.3
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FBellatrix-1.5B-xElite
+      name: Open LLM Leaderboard
+---
+<pre align="center">
+ ____  ____  __    __      __   ____  ____  ____  _  _ 
+(  _ \( ___)(  )  (  )    /__\ (_  _)(  _ \(_  _)( \/ )
+ ) _ < )__)  )(__  )(__  /(__)\  )(   )   / _)(_  )  ( 
+(____/(____)(____)(____)(__)(__)(__) (_)\_)(____)(_/\_)
+</pre>
+
+# **Bellatrix-1.5B-xElite**
+
+Bellatrix-1.5B-xElite is based on a reasoning-based model designed for the QWQ 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).
+
+# **Quickstart with Transformers**
+
+Here provides 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 = "prithivMLmods/Bellatrix-1.5B-xElite"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Give me a short introduction to large language model."
+messages = [
+    {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. 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]
+```
+
+# **Intended Use:**
+
+1. **Multilingual Dialogue Systems:**
+   - Designed for conversational AI applications, capable of handling dialogue across multiple languages.
+   - Useful in customer service, chatbots, and other dialogue-centric use cases.
+
+2. **Reasoning and QWQ Dataset Applications:**
+   - Optimized for tasks requiring logical reasoning and contextual understanding, particularly in synthetic datasets like QWQ.
+
+3. **Agentic Retrieval:**
+   - Supports retrieval-augmented generation tasks, helping systems fetch and synthesize information effectively.
+
+4. **Summarization Tasks:**
+   - Excels in summarizing long or complex text while maintaining coherence and relevance.
+
+5. **Instruction-Following Tasks:**
+   - Can execute tasks based on specific user instructions due to instruction-tuning during training.
+
+6. **Language Generation:**
+   - Suitable for generating coherent and contextually relevant text in various domains and styles.
+
+# **Limitations:**
+
+1. **Synthetic Dataset Bias:**
+   - Optimization for QWQ and similar datasets may make the model less effective on real-world or less structured data.
+
+2. **Data Dependency:**
+   - Performance may degrade on tasks or languages not well-represented in the training dataset.
+
+3. **Computational Requirements:**
+   - The optimized transformer architecture may demand significant computational resources, especially for fine-tuning or large-scale deployments.
+
+4. **Potential Hallucinations:**
+   - Like most auto-regressive models, it may generate plausible-sounding but factually incorrect or nonsensical outputs.
+
+5. **RLHF-Specific Biases:**
+   - Reinforcement Learning with Human Feedback (RLHF) can introduce biases based on the preferences of the annotators involved in the feedback process.
+
+6. **Limited Domain Adaptability:**
+   - While effective in reasoning and dialogue tasks, it may struggle with highly specialized domains or out-of-distribution tasks.
+
+7. **Multilingual Limitations:**
+   - Although optimized for multilingual use, certain low-resource languages may exhibit poorer performance compared to high-resource ones.
+
+8. **Ethical Concerns:**
+   - May inadvertently generate inappropriate or harmful content if safeguards are not applied, particularly in sensitive applications.
+
+9. **Real-Time Usability:**
+   - Latency in inference time could limit its effectiveness in real-time applications or when scaling to large user bases.
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/prithivMLmods__Bellatrix-1.5B-xElite-details)!
+Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=prithivMLmods%2FBellatrix-1.5B-xElite&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)!
+
+|      Metric       |Value (%)|
+|-------------------|--------:|
+|**Average**        |     9.55|
+|IFEval (0-Shot)    |    19.64|
+|BBH (3-Shot)       |     9.49|
+|MATH Lvl 5 (4-Shot)|    12.61|
+|GPQA (0-shot)      |     3.80|
+|MuSR (0-shot)      |     4.44|
 |MMLU-PRO (5-shot)  |     7.30|