Instructions to use sai1906/opsguard-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sai1906/opsguard-sft with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct")
model = PeftModel.from_pretrained(base_model, "sai1906/opsguard-sft")

Transformers

How to use sai1906/opsguard-sft with Transformers:

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

pipe = pipeline("text-generation", model="sai1906/opsguard-sft")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("sai1906/opsguard-sft", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sai1906/opsguard-sft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sai1906/opsguard-sft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sai1906/opsguard-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sai1906/opsguard-sft

SGLang

How to use sai1906/opsguard-sft 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 "sai1906/opsguard-sft" \
    --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": "sai1906/opsguard-sft",
		"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 "sai1906/opsguard-sft" \
        --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": "sai1906/opsguard-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sai1906/opsguard-sft with Docker Model Runner:
```
docker model run hf.co/sai1906/opsguard-sft
```

sai1906 commited on Apr 26

Commit

87085d4

verified ·

1 Parent(s): 27bedfc

Upload folder using huggingface_hub

Browse files

Files changed (1) hide show

adapter_config.json +5 -5

adapter_config.json CHANGED Viewed

@@ -16,7 +16,7 @@
   "layers_pattern": null,
   "layers_to_transform": null,
   "loftq_config": {},
-  "lora_alpha": 256,
   "lora_bias": false,
   "lora_dropout": 0.0,
   "lora_ga_config": null,
@@ -31,12 +31,12 @@
   "revision": null,
   "target_modules": [
     "o_proj",
-    "q_proj",
-    "gate_proj",
-    "up_proj",
     "k_proj",
     "v_proj",
-    "down_proj"
   ],
   "target_parameters": null,
   "task_type": "CAUSAL_LM",

   "layers_pattern": null,
   "layers_to_transform": null,
   "loftq_config": {},
+  "lora_alpha": 128,
   "lora_bias": false,
   "lora_dropout": 0.0,
   "lora_ga_config": null,
   "revision": null,
   "target_modules": [
     "o_proj",
+    "down_proj",
     "k_proj",
     "v_proj",
+    "up_proj",
+    "q_proj",
+    "gate_proj"
   ],
   "target_parameters": null,
   "task_type": "CAUSAL_LM",