Instructions to use IQuestLab/IQuest-Coder-V1-40B-Loop-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IQuestLab/IQuest-Coder-V1-40B-Loop-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="IQuestLab/IQuest-Coder-V1-40B-Loop-Instruct", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("IQuestLab/IQuest-Coder-V1-40B-Loop-Instruct", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use IQuestLab/IQuest-Coder-V1-40B-Loop-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "IQuestLab/IQuest-Coder-V1-40B-Loop-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": "IQuestLab/IQuest-Coder-V1-40B-Loop-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/IQuestLab/IQuest-Coder-V1-40B-Loop-Instruct
- SGLang
How to use IQuestLab/IQuest-Coder-V1-40B-Loop-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 "IQuestLab/IQuest-Coder-V1-40B-Loop-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": "IQuestLab/IQuest-Coder-V1-40B-Loop-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 "IQuestLab/IQuest-Coder-V1-40B-Loop-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": "IQuestLab/IQuest-Coder-V1-40B-Loop-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use IQuestLab/IQuest-Coder-V1-40B-Loop-Instruct with Docker Model Runner:
docker model run hf.co/IQuestLab/IQuest-Coder-V1-40B-Loop-Instruct
Codeforces Real Test (Div.3)
#8
by LatticeBased - opened
A: https://codeforces.com/contest/2179/problem/A
Q1: r.jina.ai/https://codeforces.com/contest/2179/problem/A
A1: Wrong Answer 1
Q2: Wrong Answer Case
A2: Wrong Answer 1
Q3: Wrong Answer Case
A3: Accepted
import sys
def main():
# Reading input from stdin
input_data = sys.stdin.read().split()
if not input_data:
return
# First value is the number of test cases
t = int(input_data[0])
idx = 1
results = []
for _ in range(t):
if idx + 1 < len(input_data):
k = int(input_data[idx])
x = int(input_data[idx+1])
idx += 2
# The constructed logic for the specified condition
# Fits (2,1)->3, (3,2)->7, (1,5)->6
if x < 1 or k < 1:
results.append(0)
elif x == 1:
results.append(k + 1)
else:
results.append(k * x + 1)
# Printing each result on a new line
for res in results:
print(res)
if __name__ == "__main__":
main()
LatticeBased changed discussion status to closed
LatticeBased changed discussion title from Codeforces Real Test to Codeforces Real Test (Div.3)