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---
license: apache-2.0
language:
- en
base_model:
- prithivMLmods/Elita-0.1-Distilled-R1-abliterated
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
- distilled
- f16
---
# **Elita-0.1-Distilled-R1-GGUF**
Elita-0.1-Distilled-R1-GGUF is based on the *Qwen [ KT ] model*, which was distilled by *DeepSeek-AI/DeepSeek-R1-Distill-Qwen-7B*. It has been fine-tuned on the long chain-of-thought reasoning model and specialized datasets, focusing on chain-of-thought (CoT) reasoning for problem-solving. This model is optimized for tasks requiring logical reasoning, detailed explanations, and multi-step problem-solving, making it ideal for applications such as instruction-following, text generation, and complex reasoning tasks.
# **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/Elita-0.1-Distilled-R1-abliterated"
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 Elita, created by DeepSeek-AI. You are a powerful reasoning 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. **Instruction-Following:** The model excels in understanding and executing detailed instructions, making it ideal for automation systems, virtual assistants, and educational tools.
2. **Text Generation:** It can produce coherent, logically structured, and contextually relevant text for use in content creation, summarization, and report writing.
3. **Complex Reasoning Tasks:** With its fine-tuning for chain-of-thought reasoning, the model is well-suited for multi-step problem-solving, logical deduction, and question-answering tasks.
4. **Research and Development:** It can support researchers and developers in exploring advancements in logical reasoning and fine-tuning methodologies.
5. **Educational Applications:** The model can assist in teaching logical reasoning and problem-solving by generating step-by-step solutions.
### **Limitations:**
1. **Domain-Specific Knowledge:** While fine-tuned on reasoning datasets, the model may lack deep expertise in highly specialized or technical domains.
2. **Hallucination:** Like many large language models, it can generate incorrect or fabricated information, especially when reasoning beyond its training data.
3. **Bias in Training Data:** The model's outputs may reflect biases present in the datasets it was fine-tuned on, which could limit its objectivity in certain contexts.
4. **Performance on Non-Reasoning Tasks:** The model is optimized for chain-of-thought reasoning and may underperform on tasks that require simpler, less structured responses.
5. **Resource-Intensive:** Running the model efficiently requires significant computational resources, which may limit accessibility for smaller-scale deployments.
6. **Dependence on Input Quality:** The model’s performance heavily depends on the clarity and quality of the input provided. Ambiguous or poorly structured prompts may yield suboptimal results. |