116 lines
4.1 KiB
Markdown
116 lines
4.1 KiB
Markdown
---
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license: apache-2.0
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base_model:
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- prithivMLmods/Sombrero-Opus-14B-Elite6
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pipeline_tag: text-generation
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tags:
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- SFT
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- text-generation-inference
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- abliterated
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- trl
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- code
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- moderately abliterated
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- math
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language:
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- en
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library_name: transformers
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---
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# **Sombrero-Opus-14B-Elite13**
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> Sombrero-Opus-14B-Elite13 builds upon the Qwen 2.5 14B modality architecture, elevating reasoning performance in mid- to large-scale models. This iteration focuses on enhancing general-purpose comprehension, structured intelligence, and interactive versatility. Fine-tuned with an advanced reasoning chain and carefully curated datasets, Elite13 offers improved contextual understanding, logical coherence, and multi-step problem-solving.
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Key improvements include:
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1. **Expanded Domain Fluency**: Delivers refined general knowledge across disciplines for more accurate and coherent answers.
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2. **Advanced Instruction Parsing**: Enhanced capacity to interpret and execute complex instructions while preserving structure and clarity.
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3. **Robust Prompt Flexibility**: Excels in adapting to diverse interaction styles, from casual inquiries to formal requests.
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4. **Extended Context Window**: Handles up to 128K tokens of input and generates up to 8K tokens in a single output — ideal for detailed reasoning and expansive replies.
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5. **Global Linguistic Range**: Offers proficiency in 29+ languages, including English, Chinese, French, Spanish, Japanese, Arabic, and more.
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---
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# **Quickstart with Transformers**
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Use the following snippet to load and test the model using `transformers` and `apply_chat_template`:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "prithivMLmods/Sombrero-Opus-14B-Elite13"
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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prompt = "What are the key principles of general-purpose AI?"
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messages = [
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{"role": "system", "content": "You are a helpful assistant capable of answering a wide range of questions."},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=512
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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```
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---
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# **Intended Use**
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1. **Cognitive Reasoning & General Q\&A**
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Designed to support high-level thinking and accurate responses across general domains.
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2. **Education & Research Support**
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Suitable for generating study guides, academic summaries, and informative explanations.
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3. **Conversational Intelligence**
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Powers AI assistants and chatbots with memory-aware, context-sensitive dialogues.
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4. **Cross-Language Communication**
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Useful in multilingual environments for translation, communication, and content creation.
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5. **Data-Aware Structuring**
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Capable of converting unstructured data into meaningful formats like JSON or tabular summaries.
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6. **Lengthy Content Generation**
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Suitable for drafting articles, technical documents, or creative prose with sustained coherence.
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---
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# **Limitations**
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1. **Resource-Intensive Execution**
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Requires robust computational infrastructure (e.g., ≥48GB VRAM) to run efficiently.
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2. **Residual Biases**
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Though tuned for neutrality, occasional bias may surface from inherited training data.
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3. **Creative Variability**
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Creative outputs such as fiction or poetry may vary in quality and style coherence.
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4. **Lack of Real-Time Knowledge**
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The model operates with a static knowledge base and lacks access to current world events.
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5. **Drift in Extended Outputs**
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Long responses may introduce cumulative inaccuracies or lose focus over time.
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6. **Prompt Dependence**
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Output quality is sensitive to the clarity and specificity of the initial prompt. |