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---
license: apache-2.0
language:
- en
base_model:
- deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
- math
- moderately abliterated
- abliterated
- code
- R1
- RL
---
# **Sombrero-R1-14B-Elite13**
> Sombrero-R1-14B-Elite13 is a fine-tuned variant of the DeepSeek-R1-Distill-Qwen-14B model, enhanced through reinforcement learning to serve as a high-performance reasoning assistant. It excels in both mathematical problem-solving and general-purpose conversational tasks. This model combines distilled efficiency with refined instruction-following behavior, offering an ideal balance of speed, capability, and coherence for complex interactive tasks.
### Key Enhancements
1. **Reinforcement Learning Fine-Tuning**
Trained with reinforcement learning objectives to optimize for alignment, reward-guided reasoning, and helpfulness in conversation.
2. **Mathematical Reasoning Proficiency**
Delivers accurate solutions and step-by-step breakdowns for algebra, calculus, number theory, logic puzzles, and applied mathematics.
3. **Instruction Adherence**
Capable of understanding and following multi-part instructions, including structured tasks and iterative refinement prompts.
4. **Expanded Context Handling**
Supports up to 128K tokens of context with output lengths up to 8K tokens, ideal for technical and educational use cases.
5. **Cross-Domain Knowledge**
Offers broad general knowledge capabilities, making it suitable for tutoring, research, and exploratory conversation across topics.
---
# **Quickstart with Transformers**
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "prithivMLmods/Sombrero-R1-14B-Elite13"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "Solve: Integrate (x^2 * e^x) dx"
messages = [
{"role": "system", "content": "You are a helpful AI assistant skilled in math and reasoning."},
{"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 Cases**
1. **Mathematics Problem Solving**
Ideal for step-by-step derivations, symbolic computation, numerical explanations, and LaTeX-supported outputs.
2. **Educational and Instructional Support**
Helpful in classrooms and learning platforms, offering guided explanations for students and instructors.
3. **Chat-based Reasoning**
Designed for coherent, context-aware dialogue generation with structured logic and continuity.
4. **Multilingual Knowledge Assistance**
Supports 29+ languages, including English, Chinese, French, German, Arabic, and others, for multilingual learning.
5. **Document and Code Explanation**
Can explain complex documents, code snippets, or structured logic flows in natural language.
---
# **Known Limitations**
1. **Compute Intensive**
Requires high-memory hardware (e.g., ≥48GB VRAM) to fully utilize context length and generation capacity.
2. **Potential for Bias and Hallucinations**
While tuned for alignment, some responses may still exhibit artifacts from pretraining biases or inaccuracies in edge cases.
3. **Drift in Long Responses**
Output may occasionally degrade in structure or accuracy across long generations.
4. **Static Knowledge**
Does not have real-time awareness or access to events or research developments post-training.
5. **Creative Task Variability**
While optimized for logic, its performance in narrative or subjective content may be inconsistent. |