Instructions to use sriram279/Leet-Reason-Qwen0.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sriram279/Leet-Reason-Qwen0.5 with Transformers:

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

pipe = pipeline("text-generation", model="sriram279/Leet-Reason-Qwen0.5")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("sriram279/Leet-Reason-Qwen0.5")
model = AutoModelForMultimodalLM.from_pretrained("sriram279/Leet-Reason-Qwen0.5")
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 sriram279/Leet-Reason-Qwen0.5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sriram279/Leet-Reason-Qwen0.5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sriram279/Leet-Reason-Qwen0.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sriram279/Leet-Reason-Qwen0.5

SGLang

How to use sriram279/Leet-Reason-Qwen0.5 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 "sriram279/Leet-Reason-Qwen0.5" \
    --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": "sriram279/Leet-Reason-Qwen0.5",
		"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 "sriram279/Leet-Reason-Qwen0.5" \
        --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": "sriram279/Leet-Reason-Qwen0.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sriram279/Leet-Reason-Qwen0.5 with Docker Model Runner:
```
docker model run hf.co/sriram279/Leet-Reason-Qwen0.5
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Leet-Reason-Qwen0.5

This model is a merged, fine-tuned version of AdithyaSK/Qwen-0.5b-Code-Reasoning-v1 on the greengerong/leetcode dataset. It is optimized to generate clean, correct Python code solutions and corresponding explanations for algorithmic programming problems.

Model Details

Base Model: AdithyaSK/Qwen-0.5b-Code-Reasoning-v1 (0.5B parameters)
Finetuning Dataset: greengerong/leetcode (2,360 programming problems and solutions)
Task: Code generation and reasoning (Python solution mapping)
Precision: 16-bit Float (Merged LoRA weights)
License: MIT

Training Configuration & Hyperparameters

The model was fine-tuned using Hugging Face's TRL SFTTrainer and PEFT (LoRA) on Windows. The following hyperparameters were used:

LoRA Configurations

LoRA Rank (r): 16
LoRA Alpha (alpha): 32
LoRA Dropout: 0.05
Target Modules: gate_proj, down_proj, k_proj, q_proj, v_proj, up_proj, o_proj

Training Hyperparameters

Epochs: 3.0
Batch Size (per device): 2
Gradient Accumulation Steps: 4
Learning Rate: 2e-4
Warmup Steps: 5
Optimizer: 8-bit AdamW (adamw_8bit)
LR Scheduler: Linear
Sequence Length (max_seq_length): 2048

Dataset Format

The training pipeline extracted the problem description (content) and mapped it to the Python solution (python) under the Alpaca instruction format:

### Instruction:
[LeetCode Problem Description]

### Response:
[Python Code Solution and Explanation]

Quickstart / Inference

You can load and query this model directly using Hugging Face Transformers:

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load the merged model
model_id = "sriram279/Leet-Reason-Qwen0.5"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto"
)

# Format your prompt
prompt = "solve Two sum problem in Python"
inputs = tokenizer.apply_chat_template(
    [{"role": "user", "content": prompt}],
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

# Generate
outputs = model.generate(
    inputs,
    max_new_tokens=512,
    temperature=0.7,
    do_sample=True
)

print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))

Framework Versions

PEFT 0.19.1
TRL 0.24.0
Transformers 4.47.0 (or newer)
PyTorch 2.5.1+cu124
Datasets 3.3.0

Downloads last month: 33

Safetensors

Model size

0.5B params

Tensor type

F16

Model tree for sriram279/Leet-Reason-Qwen0.5

Base model

AdithyaSK/Qwen-0.5b-Code-Reasoning-v1

Finetuned

(1)

this model

Quantizations

1 model