Instructions to use Kushalkhemka/CVE-OSS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kushalkhemka/CVE-OSS with Transformers:

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

pipe = pipeline("text-generation", model="Kushalkhemka/CVE-OSS")
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("Kushalkhemka/CVE-OSS")
model = AutoModelForCausalLM.from_pretrained("Kushalkhemka/CVE-OSS", 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 Kushalkhemka/CVE-OSS with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Kushalkhemka/CVE-OSS

SGLang

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

Docker Model Runner
How to use Kushalkhemka/CVE-OSS with Docker Model Runner:
```
docker model run hf.co/Kushalkhemka/CVE-OSS
```

CVE-OSS / README.md

Kushalkhemka

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7f56769 verified 11 months ago

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metadata

license: apache-2.0
language:
  - en
library_name: transformers
pipeline_tag: text-generation
base_model: openaccess-ai-collective/gpt-oss-20b
model-index:
  - name: CVE-OSS
    results: []

CVE-OSS

CVE-OSS is a 20B-parameter CVE analyst derived from the openaccess-ai-collective/gpt-oss-20b base model. It specializes in producing structured vulnerability briefs covering background, affected components, exploitation flow, impact, and mitigation guidance.

Files

The repository contains the merged BF16 weights (model-00001-of-00009.safetensors ... model-00009-of-00009.safetensors), tokenizer artifacts, and the chat template. Download via huggingface-cli download Kushalkhemka/CVE-OSS or the code sample below.

Quick Start

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Kushalkhemka/CVE-OSS"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You are a seasoned CVE analyst."},
    {"role": "user", "content": "Provide an in-depth brief on CVE-2021-3712."},
]
input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
output = model.generate(input_ids, max_new_tokens=600, temperature=0.2)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Last updated: 2025-11-28T11:07:17.076117Z