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---
license: apache-2.0
base_model: unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit
tags:
- finetuned
- lora
---
# HKT-vul-DeepSeek-R1-8b-it-v0.2
This is a LoRA fine-tuned version of [unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit).
## Training Details
- Base Model: unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit
- Fine-tuning Method: LoRA
- Merge Method: merge_and_unload()
## Usage
### Install necessary libraries
```python
import os
if "COLAB_" not in "".join(os.environ.keys()):
!pip install unsloth
else:
# Do this only in Colab notebooks! Otherwise use pip install unsloth
!pip install --no-deps bitsandbytes accelerate xformers==0.0.29.post3 peft trl==0.15.2 triton cut_cross_entropy unsloth_zoo
!pip install sentencepiece protobuf datasets huggingface_hub hf_transfer
!pip install --no-deps unsloth
```
### Install model
```python
from unsloth import FastLanguageModel
import torch
max_seq_length = 5000 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.
model_name = "weifar/unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit"
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = model_name,
max_seq_length = max_seq_length,
dtype = dtype,
load_in_4bit = load_in_4bit,
# token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)
model = FastLanguageModel.get_peft_model(
model,
r = 16, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128
target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
"gate_proj", "up_proj", "down_proj",],
lora_alpha = 16,
lora_dropout = 0, # Supports any, but = 0 is optimized
bias = "none", # Supports any, but = "none" is optimized
# [NEW] "unsloth" uses 30% less VRAM, fits 2x larger batch sizes!
use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long context
random_state = 3407,
use_rslora = False, # We support rank stabilized LoRA
loftq_config = None, # And LoftQ
)
```
### Use model
```python
FastLanguageModel.for_inference(model) # Enable native 2x faster inference
inputs = tokenizer(eval_prompt, return_tensors = "pt").to("cuda")
from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 4000)
```
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