Instructions to use Reponx/Network-Cloud-Ops-Engineer-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Reponx/Network-Cloud-Ops-Engineer-9B with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-9B")
model = PeftModel.from_pretrained(base_model, "Reponx/Network-Cloud-Ops-Engineer-9B")

Transformers

How to use Reponx/Network-Cloud-Ops-Engineer-9B with Transformers:

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

pipe = pipeline("text-generation", model="Reponx/Network-Cloud-Ops-Engineer-9B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Reponx/Network-Cloud-Ops-Engineer-9B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Reponx/Network-Cloud-Ops-Engineer-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Reponx/Network-Cloud-Ops-Engineer-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Reponx/Network-Cloud-Ops-Engineer-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Reponx/Network-Cloud-Ops-Engineer-9B

SGLang

How to use Reponx/Network-Cloud-Ops-Engineer-9B 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 "Reponx/Network-Cloud-Ops-Engineer-9B" \
    --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": "Reponx/Network-Cloud-Ops-Engineer-9B",
		"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 "Reponx/Network-Cloud-Ops-Engineer-9B" \
        --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": "Reponx/Network-Cloud-Ops-Engineer-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Reponx/Network-Cloud-Ops-Engineer-9B with Docker Model Runner:
```
docker model run hf.co/Reponx/Network-Cloud-Ops-Engineer-9B
```

dharikh commited on Sep 26

Commit

f35e37f

verified ·

1 Parent(s): 5f79aba

Update README.md

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@@ -63,6 +63,36 @@ Reponx is designed to:
 Reponx 9B is a PEFT/LoRA fine-tuned model based on **Qwen/Qwen3.5-9B**.
 ## Example
 **Prompt:**

 Reponx 9B is a PEFT/LoRA fine-tuned model based on **Qwen/Qwen3.5-9B**.
+## How to Use
+Install the required libraries:
+```bash
+pip install -U transformers peft accelerate
+```
+Load the Qwen3.5-9B base model with the Reponx adapter:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+base_model = "Qwen/Qwen3.5-9B"
+reponx_model = "Reponx/Network-Cloud-Ops-Engineer-9B"
+tokenizer = AutoTokenizer.from_pretrained(base_model)
+model = AutoModelForCausalLM.from_pretrained(
+    base_model,
+    device_map="auto",
+    dtype="auto"
+)
+model = PeftModel.from_pretrained(model, reponx_model)
+```
+> Reponx 9B is currently distributed as a **LoRA/PEFT adapter** and requires the Qwen3.5-9B base model.
 ## Example
 **Prompt:**