prithivMLmods
/

Calcium-Opus-20B-v1

@@ -1,231 +1,237 @@
----
-license: apache-2.0
-language:
-- en
-base_model:
-- Qwen/Qwen2.5-14B-Instruct
-pipeline_tag: text-generation
-library_name: transformers
-tags:
-- Opus
-- text-generation-inference
-model-index:
-- name: Calcium-Opus-20B-v1
-  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: 30.93
-      name: averaged accuracy
-    source:
-      url: >-
-        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCalcium-Opus-20B-v1
-      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: 41.81
-      name: normalized accuracy
-    source:
-      url: >-
-        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCalcium-Opus-20B-v1
-      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: 11.03
-      name: exact match
-    source:
-      url: >-
-        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCalcium-Opus-20B-v1
-      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: 13.76
-      name: acc_norm
-    source:
-      url: >-
-        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCalcium-Opus-20B-v1
-      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: 22.09
-      name: acc_norm
-    source:
-      url: >-
-        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCalcium-Opus-20B-v1
-      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: 41.49
-      name: accuracy
-    source:
-      url: >-
-        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCalcium-Opus-20B-v1
-      name: Open LLM Leaderboard
----
-![opus.gif](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/-vC9B4g2ccchvbS00HffZ.gif)
-# **Calcium-Opus-20B-v1**
-Calcium-Opus-20B-v1 is based on the Qwen 2.5 modality architecture, designed to enrich the reasoning capabilities of 20B-parameter models. These models have proven highly effective for context understanding, reasoning, and mathematical problem-solving.
-Key improvements include:
-1. **Enhanced Knowledge and Expertise**: The model demonstrates significantly more knowledge and greatly improved capabilities in coding and mathematics, thanks to specialized expert models in these domains.
-2. **Improved Instruction Following**: It shows significant advancements in following instructions, generating long texts (over 8K tokens), understanding structured data (e.g., tables), and producing structured outputs, especially in JSON format.
-3. **Better Adaptability**: The model is more resilient to diverse system prompts, enabling enhanced role-playing implementations and condition-setting for chatbots.
-4. **Long-Context Support**: It offers long-context support of up to 128K tokens and can generate up to 8K tokens in a single output.
-5. **Multilingual Proficiency**: The model supports over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.
-# **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/Calcium-Opus-20B-v1"
-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. **Reasoning and Context Understanding**:
-   Designed to assist with complex reasoning tasks, contextual understanding, and solving problems requiring logical deduction and critical thinking.
-2. **Mathematical Problem-Solving**:
-   Specialized for performing advanced mathematical reasoning and calculations, making it suitable for educational, scientific, and research-oriented applications.
-3. **Code Generation and Debugging**:
-   Offers robust support for coding tasks, including writing, debugging, and optimizing code in various programming languages, ideal for developers and software engineers.
-4. **Structured Data Analysis**:
-   Excels in processing and analyzing structured data, such as tables and JSON, and generating structured outputs, which is useful for data analysts and automation workflows.
-5. **Multilingual Applications**:
-   Supports over 29 languages, making it versatile for global applications like multilingual chatbots, content generation, and translations.
-6. **Extended Content Generation**:
-   Capable of generating long-form content (over 8K tokens), useful for writing reports, articles, and creating detailed instructional guides.
-7. **Interactive Role-Playing and Chatbots**:
-   Enhanced capabilities for role-playing and condition-setting, making it ideal for interactive chatbots, virtual assistants, and entertainment purposes.
-8. **Large-Context Tasks**:
-   With a context window of up to 128K tokens, it is ideal for analyzing or generating large documents, books, or datasets in a single session.
-# **Limitations**
-1. **Hardware Requirements**:
-   Due to its 20B parameter size and support for long-context inputs, running the model requires significant computational resources, including high-memory GPUs or TPUs.
-2. **Potential Bias in Multilingual Outputs**:
-   While it supports 29 languages, the quality and accuracy of outputs may vary depending on the language, especially for less-resourced languages.
-3. **Inconsistent Outputs for Creative Tasks**:
-   The model may occasionally produce inconsistent or repetitive results in creative writing, storytelling, or highly subjective tasks.
-4. **Limited Real-World Awareness**:
-   It lacks real-time knowledge of current events beyond its training cutoff, which may limit its ability to respond accurately to the latest information.
-5. **Error Propagation in Long-Text Outputs**:
-   In generating long texts, minor errors in early outputs can sometimes propagate, reducing the overall coherence and accuracy of the response.
-6. **Dependency on High-Quality Prompts**:
-   Performance may depend on the quality and specificity of the input prompt, requiring users to carefully design queries for optimal results.
-7. **Sensitivity to Adversarial Inputs**:
-   The model may struggle with adversarial or ambiguous inputs, leading to incorrect or irrelevant outputs.
-8. **Ethical and Safety Concerns**:
-   Potential misuse in generating misleading, harmful, or offensive content remains a concern, and guardrails must be implemented to ensure responsible use.
-# [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__Calcium-Opus-20B-v1-details)!
-Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=prithivMLmods%2FCalcium-Opus-20B-v1&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)!
-|      Metric       |Value (%)|
-|-------------------|--------:|
-|**Average**        |    26.85|
-|IFEval (0-Shot)    |    30.93|
-|BBH (3-Shot)       |    41.81|
-|MATH Lvl 5 (4-Shot)|    11.03|
-|GPQA (0-shot)      |    13.76|
-|MuSR (0-shot)      |    22.09|
 |MMLU-PRO (5-shot)  |    41.49|

+---
+license: apache-2.0
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+base_model:
+- Qwen/Qwen2.5-14B-Instruct
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- Opus
+- text-generation-inference
+model-index:
+- name: Calcium-Opus-20B-v1
+  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: 30.93
+      name: averaged accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCalcium-Opus-20B-v1
+      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: 41.81
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCalcium-Opus-20B-v1
+      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: 11.03
+      name: exact match
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCalcium-Opus-20B-v1
+      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: 13.76
+      name: acc_norm
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCalcium-Opus-20B-v1
+      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: 22.09
+      name: acc_norm
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCalcium-Opus-20B-v1
+      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: 41.49
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FCalcium-Opus-20B-v1
+      name: Open LLM Leaderboard
+---
+![opus.gif](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/-vC9B4g2ccchvbS00HffZ.gif)
+# **Calcium-Opus-20B-v1**
+Calcium-Opus-20B-v1 is based on the Qwen 2.5 modality architecture, designed to enrich the reasoning capabilities of 20B-parameter models. These models have proven highly effective for context understanding, reasoning, and mathematical problem-solving.
+Key improvements include:
+1. **Enhanced Knowledge and Expertise**: The model demonstrates significantly more knowledge and greatly improved capabilities in coding and mathematics, thanks to specialized expert models in these domains.
+2. **Improved Instruction Following**: It shows significant advancements in following instructions, generating long texts (over 8K tokens), understanding structured data (e.g., tables), and producing structured outputs, especially in JSON format.
+3. **Better Adaptability**: The model is more resilient to diverse system prompts, enabling enhanced role-playing implementations and condition-setting for chatbots.
+4. **Long-Context Support**: It offers long-context support of up to 128K tokens and can generate up to 8K tokens in a single output.
+5. **Multilingual Proficiency**: The model supports over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.
+# **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/Calcium-Opus-20B-v1"
+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. **Reasoning and Context Understanding**:
+   Designed to assist with complex reasoning tasks, contextual understanding, and solving problems requiring logical deduction and critical thinking.
+2. **Mathematical Problem-Solving**:
+   Specialized for performing advanced mathematical reasoning and calculations, making it suitable for educational, scientific, and research-oriented applications.
+3. **Code Generation and Debugging**:
+   Offers robust support for coding tasks, including writing, debugging, and optimizing code in various programming languages, ideal for developers and software engineers.
+4. **Structured Data Analysis**:
+   Excels in processing and analyzing structured data, such as tables and JSON, and generating structured outputs, which is useful for data analysts and automation workflows.
+5. **Multilingual Applications**:
+   Supports over 29 languages, making it versatile for global applications like multilingual chatbots, content generation, and translations.
+6. **Extended Content Generation**:
+   Capable of generating long-form content (over 8K tokens), useful for writing reports, articles, and creating detailed instructional guides.
+7. **Interactive Role-Playing and Chatbots**:
+   Enhanced capabilities for role-playing and condition-setting, making it ideal for interactive chatbots, virtual assistants, and entertainment purposes.
+8. **Large-Context Tasks**:
+   With a context window of up to 128K tokens, it is ideal for analyzing or generating large documents, books, or datasets in a single session.
+# **Limitations**
+1. **Hardware Requirements**:
+   Due to its 20B parameter size and support for long-context inputs, running the model requires significant computational resources, including high-memory GPUs or TPUs.
+2. **Potential Bias in Multilingual Outputs**:
+   While it supports 29 languages, the quality and accuracy of outputs may vary depending on the language, especially for less-resourced languages.
+3. **Inconsistent Outputs for Creative Tasks**:
+   The model may occasionally produce inconsistent or repetitive results in creative writing, storytelling, or highly subjective tasks.
+4. **Limited Real-World Awareness**:
+   It lacks real-time knowledge of current events beyond its training cutoff, which may limit its ability to respond accurately to the latest information.
+5. **Error Propagation in Long-Text Outputs**:
+   In generating long texts, minor errors in early outputs can sometimes propagate, reducing the overall coherence and accuracy of the response.
+6. **Dependency on High-Quality Prompts**:
+   Performance may depend on the quality and specificity of the input prompt, requiring users to carefully design queries for optimal results.
+7. **Sensitivity to Adversarial Inputs**:
+   The model may struggle with adversarial or ambiguous inputs, leading to incorrect or irrelevant outputs.
+8. **Ethical and Safety Concerns**:
+   Potential misuse in generating misleading, harmful, or offensive content remains a concern, and guardrails must be implemented to ensure responsible use.
+# [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__Calcium-Opus-20B-v1-details)!
+Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=prithivMLmods%2FCalcium-Opus-20B-v1&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)!
+|      Metric       |Value (%)|
+|-------------------|--------:|
+|**Average**        |    26.85|
+|IFEval (0-Shot)    |    30.93|
+|BBH (3-Shot)       |    41.81|
+|MATH Lvl 5 (4-Shot)|    11.03|
+|GPQA (0-shot)      |    13.76|
+|MuSR (0-shot)      |    22.09|
 |MMLU-PRO (5-shot)  |    41.49|