Instructions to use Vinay-11/Llama-lora-instruct-finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Vinay-11/Llama-lora-instruct-finetuning with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Vinay-11/Llama-lora-instruct-finetuning")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Vinay-11/Llama-lora-instruct-finetuning", dtype="auto")

PEFT
How to use Vinay-11/Llama-lora-instruct-finetuning with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Vinay-11/Llama-lora-instruct-finetuning with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Vinay-11/Llama-lora-instruct-finetuning"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vinay-11/Llama-lora-instruct-finetuning",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Vinay-11/Llama-lora-instruct-finetuning

SGLang

How to use Vinay-11/Llama-lora-instruct-finetuning with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Vinay-11/Llama-lora-instruct-finetuning" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vinay-11/Llama-lora-instruct-finetuning",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Vinay-11/Llama-lora-instruct-finetuning" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vinay-11/Llama-lora-instruct-finetuning",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Vinay-11/Llama-lora-instruct-finetuning with Docker Model Runner:
```
docker model run hf.co/Vinay-11/Llama-lora-instruct-finetuning
```

You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

LLaMA LoRA Instruction Fine-Tuning (FP16)

This repository contains LoRA adapter weights for a LLaMA language model fine-tuned on instruction-style question–answer data using FP16 precision. The goal is to improve instruction following and reasoning while keeping training efficient via parameter-efficient fine-tuning (PEFT).

Model Details

Base Model: LLaMA

Fine-Tuning Method: LoRA (Low-Rank Adaptation)

Precision: FP16

Task: Instruction Following / Question Answering

Frameworks: Hugging Face Transformers, PEFT, PyTorch

Checkpoint Format: .safetensors

🔹 This repository contains LoRA adapter weights only. 🔹 The base LLaMA model must be loaded separately.

Dataset

The model was fine-tuned using the FineTome-100k dataset, a curated collection of high-quality instruction–response pairs designed for supervised fine-tuning (SFT) of large language models.

Dataset Name: FineTome-100k

Type: Instruction / Q&A pairs

Size: ~100K samples

🔗 Dataset Link: https://huggingface.co/datasets/mlabonne/FineTome-100k

Training Procedure

Objective: Causal Language Modeling

Trainable Parameters: LoRA adapters only

Frozen Parameters: All base model weights

Optimizer: AdamW

Precision: FP16

Approach: Supervised Fine-Tuning (SFT)

This setup allows efficient adaptation of a large model without updating all parameters.

How to Use Requirements pip install transformers peft accelerate torch

Load Model with LoRA Adapters from transformers import AutoModelForCausalLM, AutoTokenizer from peft import PeftModel import torch

base_model_id = "meta-llama/Llama-2-7b-hf" adapter_id = "Vinay-11/Llama-lora-instruct-finetuning"

tokenizer = AutoTokenizer.from_pretrained(base_model_id)

base_model = AutoModelForCausalLM.from_pretrained( base_model_id, torch_dtype=torch.float16, device_map="auto" )

model = PeftModel.from_pretrained(base_model, adapter_id) model.eval()

Example Inference prompt = "What is LoRA fine-tuning?"

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate( **inputs, max_new_tokens=120, temperature=0.7, do_sample=True )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Downloads last month: -; Downloads are not tracked for this model. How to track

Model tree for Vinay-11/Llama-lora-instruct-finetuning

Base model

meta-llama/Llama-2-7b-hf

Adapter

(2394)

this model

Vinay-11
/

Llama-lora-instruct-finetuning

You need to agree to share your contact information to access this model

Model tree for Vinay-11/Llama-lora-instruct-finetuning

Dataset used to train Vinay-11/Llama-lora-instruct-finetuning