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---
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|
|MMLU-PRO (5-shot)  |     7.30|