---
license: bsl-1.0
language:
- en
metrics:
- accuracy
base_model:
- unsloth/DeepSeek-R1-Distill-Llama-8B
pipeline_tag: text-generation
library_name: transformers
---
# DeepSeek-R1-Distill-Llama-8B-ZABBIX-BIT

**Model Overview:**  
---
language: en
tags:
  - zabbix
  - fine-tuning
  - lora
  - text-generation
license: Apache-2.0
finetuned_from: unsloth/DeepSeek-R1-Distill-Llama-8B
---

# DeepSeek-R1-Distill-Llama-8B-ZABBIX-BIT

**Model Overview:**  
This model is a fine-tuned version of the [DeepSeek-R1-Distill-Llama-8B](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Llama-8B) model, optimized specifically for addressing technical questions related to Zabbix monitoring, alerting, and performance optimization. The model leverages chain-of-thought reasoning to provide detailed, step-by-step answers.

**Fine-Tuning Details:**  
- **Base Model:** DeepSeek-R1-Distill-Llama-8B  
- **Fine-Tuning Method:** Supervised Fine-Tuning (SFT) using LoRA (Low-Rank Adaptation)  
- **LoRA Configuration:**  
  - **Rank (r):** 16  
  - **Target Modules:** `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`  
  - **LoRA Alpha:** 16  
  - **Dropout:** 0 (No dropout applied)  
  - **Gradient Checkpointing:** Enabled (via Unsloth optimizations)  
- **Quantization:** 4-bit quantization for improved memory efficiency  
- **Dataset:** Fine-tuned on a custom JSON dataset (`questions_with_cot_and_answers.json`) containing Zabbix-related questions, chain-of-thought explanations, and corresponding answers  
- **Training Framework:** Utilized the `SFTTrainer` from the TRL library and Unsloth’s fast inference optimizations

**Intended Use Cases:**  
This model is designed to support IT professionals and system administrators in:
- Answering complex Zabbix configuration and performance optimization questions  
- Providing detailed, reasoning-based responses for technical troubleshooting  
- Enhancing educational content and technical support regarding Zabbix environments

**Usage Example:**

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "aman-ph/DeepSeek-R1-Distill-Llama-8B-ZABBIX-BIT"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = """Below is an instruction that describes a task, paired with an input that provides further context.
Write a response that appropriately completes the request.
Before answering, think carefully about the question and create a step-by-step chain of thoughts to ensure a logical and accurate response.

### Instruction:
You are a Zabbix expert with advanced knowledge in monitoring, alerting, and performance optimization.
Please answer the following Zabbix-related question.

### Question:
what is the best way to configure a proxygroup in Zabbix?

### Response:
<think>"""

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(inputs.input_ids, max_new_tokens=200)
print(tokenizer.decode(outputs[0]))