Instructions to use RealMythos/pocwriter-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RealMythos/pocwriter-v1 with Transformers:

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

pipe = pipeline("text-generation", model="RealMythos/pocwriter-v1")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("RealMythos/pocwriter-v1")
model = AutoModelForMultimodalLM.from_pretrained("RealMythos/pocwriter-v1")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RealMythos/pocwriter-v1 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/RealMythos/pocwriter-v1

SGLang

How to use RealMythos/pocwriter-v1 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 "RealMythos/pocwriter-v1" \
    --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": "RealMythos/pocwriter-v1",
		"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 "RealMythos/pocwriter-v1" \
        --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": "RealMythos/pocwriter-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use RealMythos/pocwriter-v1 with Docker Model Runner:
```
docker model run hf.co/RealMythos/pocwriter-v1
```

pocwriter-v1 / README.md

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	---
	base_model: Qwen/Qwen3.5-9B
	datasets:
	- RealMythos/RealMythosReasoning
	library_name: transformers
	pipeline_tag: text-generation
	license: apache-2.0
	language:
	- en
	tags:
	- qwen3_5
	- code
	- security
	- cybersecurity
	- vulnerability-detection
	- proof-of-concept
	- cve
	- cwe
	- llama-factory
	- sft
	---

	# RealMythos/pocwriter-v1

	`pocwriter-v1` is a full-parameter supervised fine-tune of Qwen3.5-9B, specialized for
	security research: source-code vulnerability discovery/analysis and proof-of-concept (PoC)
	generation for authorized testing. It is trained on
	[RealMythos/RealMythosReasoning](https://huggingface.co/datasets/RealMythos/RealMythosReasoning),
	a CVE-grounded C/C++ vulnerability-reasoning dataset.

	> Stage: stage-1 SFT, `checkpoint-748`. This is an early/intermediate checkpoint — see Limitations.

	## Intended use

	Built to assist defensive and authorized offensive security work:

	- Vulnerability mining — spotting likely-vulnerable patterns in C/C++ source and explaining the bug class (with a focus on memory-safety issues).
	- PoC drafting — generating proof-of-concept code to validate a finding on a target you are
	authorized to test (pentest engagements, CTF, your own systems, security research).
	- Triage & write-ups — prioritizing findings, drafting reproduction steps and remediation advice.

	### Out of scope / responsible use

	Do not use this model against systems you do not own or lack explicit written authorization to test.
	Generated PoCs are intended for validation in controlled, authorized environments only. Users are
	solely responsible for complying with applicable laws and for any consequences of use.

	## Training data

	Trained on [RealMythos/RealMythosReasoning](https://huggingface.co/datasets/RealMythos/RealMythosReasoning)
	(CC-BY-4.0):

	- 6,159 examples, each tied to a unique real-world CVE (~177 MB), English.
	- Each record pairs a vulnerability-analysis prompt + code context with CVE/CWE/project metadata,
	reasoning traces, a final response, and PoC evaluation scores (relevance / exploitability).
	- Heavily weighted toward memory-safety classes — top CWEs: CWE-119 (buffer errors), CWE-125
	(out-of-bounds read), CWE-787 (out-of-bounds write).
	- Uses patch-unaware reasoning cleanup to reduce leakage from fixed-code information, plus quality-control review flags.

	## Training setup

	\| \| \|
	\|---\|---\|
	\| Base model \| Qwen3.5-9B (`Qwen3_5ForConditionalGeneration`) \|
	\| Method \| Full-parameter supervised fine-tuning (SFT) \|
	\| Framework \| [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) \|
	\| Distributed \| DeepSpeed ZeRO \|
	\| Checkpoint \| stage-1, global step 748 \|
	\| Precision \| bf16 \|

	## Usage

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_id = "RealMythos/pocwriter-v1"
	tok = AutoTokenizer.from_pretrained(model_id)
	model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")

	messages = [
	{"role": "user", "content": "Analyze this function for memory-safety issues and, if any, draft a PoC:\n<code here>"},
	]
	inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
	out = model.generate(inputs, max_new_tokens=512)
	print(tok.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))
	```

	> If this is the multimodal variant of the base, load it with the matching `AutoProcessor` /
	> `AutoModelForImageTextToText` class instead.

	## Limitations

	- Intermediate stage-1 checkpoint; outputs may be unstable, incomplete, or change in later stages.
	- Trained primarily on C/C++ memory-safety CVEs — weaker outside that distribution (other languages / bug classes).
	- May hallucinate vulnerabilities or emit non-working PoCs — always verify manually.
	- Inherits the biases, knowledge cutoff, and license terms of the Qwen3.5-9B base model.

	## Citation

	Built on the [RealMythos](https://huggingface.co/RealMythos) effort to reconstruct open-source
	security-reasoning infrastructure. If you use this model, please credit both the model and the
	[RealMythosReasoning](https://huggingface.co/datasets/RealMythos/RealMythosReasoning) dataset.