117 lines
4.1 KiB
Markdown
117 lines
4.1 KiB
Markdown
|
---
|
||
|
|
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.
|