Instructions to use DhruvSIngh9911/Qwen-Adyapak-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DhruvSIngh9911/Qwen-Adyapak-7B with Transformers:

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

pipe = pipeline("text-generation", model="DhruvSIngh9911/Qwen-Adyapak-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DhruvSIngh9911/Qwen-Adyapak-7B")
model = AutoModelForCausalLM.from_pretrained("DhruvSIngh9911/Qwen-Adyapak-7B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DhruvSIngh9911/Qwen-Adyapak-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DhruvSIngh9911/Qwen-Adyapak-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DhruvSIngh9911/Qwen-Adyapak-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DhruvSIngh9911/Qwen-Adyapak-7B

SGLang

How to use DhruvSIngh9911/Qwen-Adyapak-7B 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 "DhruvSIngh9911/Qwen-Adyapak-7B" \
    --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": "DhruvSIngh9911/Qwen-Adyapak-7B",
		"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 "DhruvSIngh9911/Qwen-Adyapak-7B" \
        --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": "DhruvSIngh9911/Qwen-Adyapak-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use DhruvSIngh9911/Qwen-Adyapak-7B with Docker Model Runner:
```
docker model run hf.co/DhruvSIngh9911/Qwen-Adyapak-7B
```

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Fine-Tuning Qwen2.5-Math-7B with LoRA (8-bit Quantization)

This repository demonstrates how to fine-tune the Qwen2.5-Math-7B model using LoRA in 8-bit precision for efficient memory usage. The process combines four CSV datasets (main_train.csv, main_test.csv, socratic_train.csv, socratic_test.csv) and produces a LoRA-adapted model for solving math/algebra problems.

Overview
Requirements
Installation
Data Preparation
Fine-Tuning Steps
Inference
Merging LoRA Weights (Optional)
License

Overview

Base Model: Qwen2.5-Math-7B
Parameter-Efficient Fine-Tuning: LoRA (Low-Rank Adaptation)
Quantization: 8-bit, using bitsandbytes
Datasets: Four CSV files:
- main_train.csv & main_test.csv
- socratic_train.csv & socratic_test.csv

The code merges these training sets and fine-tunes the model to handle algebraic problem-solving tasks in a memory-efficient manner.

Requirements

Python 3.8+
PyTorch (tested with torch>=2.0.0)
Transformers (tested with transformers>=4.30.0)
PEFT (peft>=0.4.0)
Datasets (datasets>=2.10.0)
Accelerate (accelerate>=0.20.0)
bitsandbytes (bitsandbytes>=0.39.0)
pandas (for CSV reading)

Installation

git clone <this-repo-url>
cd <this-repo-folder>

# Install Python dependencies
pip install torch transformers datasets peft accelerate bitsandbytes pandas



## Data Preperation

- main_train.csv
- main_test.csv
- socratic_train.csv
- socratic_test.csv

Downloads last month: -

Safetensors

Model size

8B params

Tensor type

F32

F16

Model tree for DhruvSIngh9911/Qwen-Adyapak-7B

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-Math-7B

Quantized

(15)

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