Instructions to use orucexe/Qwen3-8B-Algorithmic-Code-Analyzer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use orucexe/Qwen3-8B-Algorithmic-Code-Analyzer with Transformers:

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

pipe = pipeline("text-generation", model="orucexe/Qwen3-8B-Algorithmic-Code-Analyzer")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("orucexe/Qwen3-8B-Algorithmic-Code-Analyzer")
model = AutoModelForCausalLM.from_pretrained("orucexe/Qwen3-8B-Algorithmic-Code-Analyzer", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use orucexe/Qwen3-8B-Algorithmic-Code-Analyzer with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "orucexe/Qwen3-8B-Algorithmic-Code-Analyzer"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "orucexe/Qwen3-8B-Algorithmic-Code-Analyzer",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/orucexe/Qwen3-8B-Algorithmic-Code-Analyzer

SGLang

How to use orucexe/Qwen3-8B-Algorithmic-Code-Analyzer 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 "orucexe/Qwen3-8B-Algorithmic-Code-Analyzer" \
    --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": "orucexe/Qwen3-8B-Algorithmic-Code-Analyzer",
		"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 "orucexe/Qwen3-8B-Algorithmic-Code-Analyzer" \
        --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": "orucexe/Qwen3-8B-Algorithmic-Code-Analyzer",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use orucexe/Qwen3-8B-Algorithmic-Code-Analyzer with Docker Model Runner:
```
docker model run hf.co/orucexe/Qwen3-8B-Algorithmic-Code-Analyzer
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen3-8B Algorithmic Code Analyzer

Experimental Qwen3-8B fine-tune for exploring C++ code explanations, algorithm ideas, invariants, complexity and explanation checking. Trained on 3,613 problem → code + analysis examples; supplied-code analysis is an exploratory use.

Usage

pip install "transformers>=4.51,<5" accelerate torch

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

repo = "orucexe/Qwen3-8B-Algorithmic-Code-Analyzer"
tokenizer = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(
    repo, torch_dtype="auto", device_map="auto"
).eval()

prompt = """Explain what this C++ code computes, its core idea,
loop invariant, time and auxiliary-space complexity, and edge cases.
Do not rewrite the code.

long long sum(const std::vector<int>& a) {
    long long s = 0;
    for (int x : a) s += x;
    return s;
}
"""
inputs = tokenizer.apply_chat_template(
    [{"role": "user", "content": prompt}],
    add_generation_prompt=True, enable_thinking=False,
    return_dict=True, return_tensors="pt"
).to(model.device)
with torch.inference_mode():
    output = model.generate(
        **inputs, max_new_tokens=1024, do_sample=True,
        temperature=0.7, top_p=0.8, top_k=20,
        pad_token_id=tokenizer.eos_token_id
    )
print(tokenizer.decode(output[0, inputs.input_ids.shape[1]:], skip_special_tokens=True))