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

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
```

8 tps on nVidia H200

#17

by svilen333 - opened Jan 10

Discussion

svilen333

Jan 10

Hi, I am testing the model on 1 x nVidia H200 with latest vLLM, is it normal to get 8 tps using 128K context or I am doing something wrong?

malithh

Jan 10

Hi
That is not normal for sure, how many concurrent request are you doing?

svilen333

Jan 10

Only one request. Using the BF16 version.

malithh

Jan 10

•

edited Jan 10

Yea then something is wrong, the auto calibrator might not have picked up the top_k and top parameters. Whats your input length and output length on test ?

svilen333

Jan 10

Input length 15 tokens, output is over 1000. Just gave task to code html+js simple task.

malithh

Jan 12

•

edited Jan 12

Question is there a big difference between IQuestLab/IQuest-Coder-V1-40B-Loop-Instruct and IQuestLab/IQuest-Coder-V1-40B-Instruct ? In terms of output

I’m part of an early-stage custom inference stack called DeployPad (https://www.geodd.io). We are able to run models 50% faster than vanilla. Our team was able to run IQuest-Coder-V1-40B-Instruct on single h200 at 80 tps per user. Would you like to try it out? This is still early beta and we’re running more tests, but the results so far look promising. Want to extend our support to Models from IQuestLab. Like the the Loop Instruct

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