Instructions to use Falah/my-qlora-mistral7b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Falah/my-qlora-mistral7b-instruct with Transformers:

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

pipe = pipeline("text-generation", model="Falah/my-qlora-mistral7b-instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Falah/my-qlora-mistral7b-instruct", dtype="auto")

PEFT
How to use Falah/my-qlora-mistral7b-instruct with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Falah/my-qlora-mistral7b-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Falah/my-qlora-mistral7b-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Falah/my-qlora-mistral7b-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Falah/my-qlora-mistral7b-instruct

SGLang

How to use Falah/my-qlora-mistral7b-instruct 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 "Falah/my-qlora-mistral7b-instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Falah/my-qlora-mistral7b-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Falah/my-qlora-mistral7b-instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Falah/my-qlora-mistral7b-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Falah/my-qlora-mistral7b-instruct with Docker Model Runner:
```
docker model run hf.co/Falah/my-qlora-mistral7b-instruct
```

my-qlora-mistral7b-instruct

This is a QLoRA fine-tuned version of the mistralai/Mistral-7B-Instruct-v0.2 model. It was fine-tuned using Low-Rank Adaptation (LoRA) in 4-bit precision for efficiency on consumer GPUs.

🚀 Model Details

Base model: mistralai/Mistral-7B-Instruct-v0.2
Fine-tuning method: QLoRA with PEFT
Quantization: 4-bit (bitsandbytes)
Task: Instruction following / conversational AI
Dataset: Custom instruction-response pairs
Training environment: Google Colab Pro (T4 / A100 GPU)

📦 How to Use

# First, make sure you have the necessary libraries installed:
# pip install transformers peft bitsandbytes accelerate

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, BitsAndBytesConfig
from peft import PeftModel
from accelerate import infer_auto_device_map, dispatch_model

fine_tuned_model_id = "Falah/my-qlora-mistral7b-instruct"
base_model_id = "mistralai/Mistral-7B-Instruct-v0.2"

print("Loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(fine_tuned_model_id)

print("Loading base model with quantization...")
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16
)
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    quantization_config=bnb_config,
    device_map=None, # Load to CPU initially
    torch_dtype=torch.float16,
    trust_remote_code=True,
)

print("Loading PEFT adapter onto the base model...")
model = PeftModel.from_pretrained(base_model, fine_tuned_model_id)

print("Dispatching model to devices...")
device_map = infer_auto_device_map(model, dtype=torch.float16)
model = dispatch_model(model, device_map=device_map)

# Ensure the model is in evaluation mode
model.eval()

print("Creating text generation pipeline...")
generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.float16,
    device_map="auto",
)

# Define a sample user prompt
user_prompt = "Write a short story about a robot learning to love."

# Format the prompt
formatted_prompt = f"[INST] {user_prompt} [/INST]"

# Generate text
outputs = generator(
    formatted_prompt,
    max_new_tokens=200,
    num_return_sequences=1,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95,
)

# Print the generated text
for i, output in enumerate(outputs):
    print(f"Generated Output {i+1}:\n{output['generated_text']}")

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