---
language: en
license: apache-2.0
library: transformers
tags:
- deepseek
- llama
- lora
- text-generation
- hr-assistant
base_model: deepseek-ai/DeepSeek-R1-Distill-Llama-8B
datasets:
- sa16feb24/jmpers1
pipeline_tag: text-generation
library_name: transformers
---

# jmpers-lig-auto-1-dsr1-l8b <button class="citation-flag" data-index="5">

This is a fine-tuned version of the **DeepSeek-R1-Distill-Llama-8B** model <button class="citation-flag" data-index="1"> using the `sa16feb24/jmpers1` dataset via Lightning AI. The model is merged with LoRA adapters for efficient deployment <button class="citation-flag" data-index="3">.

## Model Details
- **Base Model:** [deepseek-ai/DeepSeek-R1-Distill-Llama-8B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B)
- **Fine-tuning Method:** LoRA (merged)
- **Dataset:** [sa16feb24/jmpers1](https://huggingface.co/datasets/sa16feb24/jmpers1)
- **Parameters:**  
  ```python
  LoraConfig(
      r=16,
      lora_alpha=32,
      target_modules=["q_proj", "v_proj"],
      lora_dropout=0.1,
      bias="none",
      task_type="CAUSAL_LM"
  )

# Usage
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "sa16feb24/jmpers-lig-auto-1-dsr1-l8b",
    torch_dtype="auto"
)
tokenizer = AutoTokenizer.from_pretrained(
    "sa16feb24/jmpers-lig-auto-1-dsr1-l8b"
)

# Example inference
inputs = tokenizer("How did the AI chatbot project improve HR efficiency?", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0]))

# Training Configuration 7
Hardware: NVIDIA T4 GPU (Colab)
Optimizations:
8-bit quantization
Gradient checkpointing
Mixed precision (FP16)