Instructions to use athulkrishnan/BountyHound-Coder-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use athulkrishnan/BountyHound-Coder-14B with Transformers:

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

pipe = pipeline("text-generation", model="athulkrishnan/BountyHound-Coder-14B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("athulkrishnan/BountyHound-Coder-14B")
model = AutoModelForCausalLM.from_pretrained("athulkrishnan/BountyHound-Coder-14B")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

PEFT
How to use athulkrishnan/BountyHound-Coder-14B with PEFT:
```
Task type is invalid.
```

llama-cpp-python

How to use athulkrishnan/BountyHound-Coder-14B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="athulkrishnan/BountyHound-Coder-14B",
	filename="gguf/BountyHound-Coder-14B-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use athulkrishnan/BountyHound-Coder-14B with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf athulkrishnan/BountyHound-Coder-14B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf athulkrishnan/BountyHound-Coder-14B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf athulkrishnan/BountyHound-Coder-14B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf athulkrishnan/BountyHound-Coder-14B:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf athulkrishnan/BountyHound-Coder-14B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf athulkrishnan/BountyHound-Coder-14B:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf athulkrishnan/BountyHound-Coder-14B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf athulkrishnan/BountyHound-Coder-14B:Q4_K_M

Use Docker

docker model run hf.co/athulkrishnan/BountyHound-Coder-14B:Q4_K_M

LM Studio
Jan

vLLM

How to use athulkrishnan/BountyHound-Coder-14B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "athulkrishnan/BountyHound-Coder-14B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "athulkrishnan/BountyHound-Coder-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/athulkrishnan/BountyHound-Coder-14B:Q4_K_M

SGLang

How to use athulkrishnan/BountyHound-Coder-14B 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 "athulkrishnan/BountyHound-Coder-14B" \
    --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": "athulkrishnan/BountyHound-Coder-14B",
		"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 "athulkrishnan/BountyHound-Coder-14B" \
        --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": "athulkrishnan/BountyHound-Coder-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use athulkrishnan/BountyHound-Coder-14B with Ollama:
```
ollama run hf.co/athulkrishnan/BountyHound-Coder-14B:Q4_K_M
```

Unsloth Studio

How to use athulkrishnan/BountyHound-Coder-14B with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for athulkrishnan/BountyHound-Coder-14B to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for athulkrishnan/BountyHound-Coder-14B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for athulkrishnan/BountyHound-Coder-14B to start chatting

How to use athulkrishnan/BountyHound-Coder-14B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf athulkrishnan/BountyHound-Coder-14B:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "athulkrishnan/BountyHound-Coder-14B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use athulkrishnan/BountyHound-Coder-14B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf athulkrishnan/BountyHound-Coder-14B:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default athulkrishnan/BountyHound-Coder-14B:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use athulkrishnan/BountyHound-Coder-14B with Docker Model Runner:
```
docker model run hf.co/athulkrishnan/BountyHound-Coder-14B:Q4_K_M
```

Lemonade

How to use athulkrishnan/BountyHound-Coder-14B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull athulkrishnan/BountyHound-Coder-14B:Q4_K_M

Run and chat with the model

lemonade run user.BountyHound-Coder-14B-Q4_K_M

List all available models

lemonade list

Request access to BountyHound-Coder-14B

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

BountyHound is released for AUTHORIZED security research and defensive triage only. By requesting access you confirm that you will use this model ONLY against assets you are explicitly authorized to test (in-scope bug-bounty programs, systems you own, or written penetration-test/red-team engagements), that you will follow coordinated / responsible disclosure, and that you accept the "as-is, no warranty" terms in the Disclaimer section of this card. Access is granted at the maintainer's discretion.

🐕‍🦺 BountyHound-Coder-14B

A gated, security-specialised co-pilot for bug-bounty triage and recon prioritisation, fine-tuned from Qwen2.5-Coder-14B-Instruct. Built to run locally on a single 16 GB GPU.

BountyHound is not an autonomous hacking agent. It is a decision-support model that helps an authorized researcher answer two questions fast and well:

"Is this finding real and worth submitting?" — submit/kill triage, impact reasoning, false-positive and out-of-scope filtering.
"Where should I look first?" — recon attack-surface ranking: a tech stack in, a prioritised, rationale-backed vulnerability-class hit-list out.

It is deliberately terse and impact-first: it kills weak findings, asks for proof of exploitation, and avoids "could potentially" filler.

Gated and authorized-use-only. Access requires approval. See Intended use, Bias, Risks & Limitations, and the Disclaimer before requesting.

Model information


Developer	`athulkrishnan` (independent)
Model type	Auto-regressive transformer (decoder-only), instruction-tuned
Base model	`Qwen/Qwen2.5-Coder-14B-Instruct` (~14.7B params, 48 layers)
Fine-tune method	QLoRA SFT (4-bit NF4 base, LoRA r=32) via Unsloth + TRL
Specialisation	Bug-bounty finding triage/validation · recon attack-surface ranking
Language	English
Context length	32,768 native (up to 131K with YaRN); trained at 2,048
Precision / formats	Merged BF16 safetensors · Q4_K_M GGUF in `gguf/`
License	Apache-2.0 (inherited from the Qwen base)
Status	Static, offline fine-tune · v1 (see Versions)

Intended use

Intended use cases

Finding triage & validation — decide submit vs. kill, sanity-check severity, reason about real-world impact, and cut duplicate / informational / out-of-scope noise before a human writes a report.
Recon prioritisation — turn a fingerprinted tech stack or attack surface into a ranked hit-list of vulnerability classes worth testing first, with one-line rationale.
Methodology assistant — explain bug classes, CWE mappings, and report framing to support authorized learning and assessment work.

Downstream use

A local triage/ranking step inside an authorized bug-bounty or pentest workflow (human-in-the-loop), e.g. pre-filtering scanner output or drafting impact statements.
A base for further domain fine-tuning or for pairing with retrieval (RAG) over fresh CVEs / current program scope.

Out-of-scope and prohibited use

Testing, scanning, or exploiting systems you are not explicitly authorized to assess.
Autonomous attack execution without human review — BountyHound is a co-pilot, not an agent.
Generating malware, phishing, or weaponised exploit payloads for unauthorized use.
Treating outputs as ground truth, or as legal/compliance advice. Always validate.
Any use that violates applicable law or platform/program rules.

How to get started

Requirements

transformers >= 4.40 (developed on 4.56.2), torch >= 2.3, and accelerate. The merged model is BF16 (~29 GB); for a single 16 GB GPU use the Q4_K_M GGUF with llama.cpp / Ollama, or load in 4-bit with bitsandbytes.

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "athulkrishnan/BountyHound-Coder-14B"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype="auto", device_map="auto")

SYSTEM = (
    "You are a bug-bounty co-pilot for an authorized security researcher. You assist ONLY "
    "with testing that is in-scope and authorized on bug-bounty programs. You are sharp, "
    "terse, and impact-first: you kill weak findings, prove real exploitation, and never pad "
    "reports with 'could potentially'. Your specialties are finding triage/validation and "
    "recon attack-surface ranking."
)
messages = [
    {"role": "system", "content": SYSTEM},
    {"role": "user", "content":
        "Triage: reflected XSS on a marketing page, unauthenticated, no session context. "
        "Submit or kill? One line + why."},
]
ids = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(ids, max_new_tokens=256, temperature=0.3, top_p=0.9)
print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))

Ollama / llama.cpp (GGUF)

Download gguf/BountyHound-Coder-14B-Q4_K_M.gguf, then create a Modelfile:

FROM ./BountyHound-Coder-14B-Q4_K_M.gguf
TEMPLATE """{{- if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{- range .Messages }}<|im_start|>{{ .Role }}
{{ .Content }}<|im_end|>
{{ end }}<|im_start|>assistant
"""
SYSTEM """You are a bug-bounty co-pilot for an authorized security researcher. You assist ONLY with testing that is in-scope and authorized on bug-bounty programs. You are sharp, terse, and impact-first: you kill weak findings, prove real exploitation, and never pad reports with 'could potentially'. Your specialties are finding triage/validation and recon attack-surface ranking."""
PARAMETER temperature 0.3
PARAMETER top_p 0.9
PARAMETER stop "<|im_start|>"
PARAMETER stop "<|im_end|>"

ollama create bountyhound -f Modelfile
ollama run bountyhound "Rank the attack surface for a Spring Boot + GraphQL + S3 stack."

Prompt format

Qwen2.5 ChatML (<|im_start|>role … <|im_end|>) with the security system prompt above. Recommended decoding: temperature 0.3, top_p 0.9, repeat_penalty 1.05.

Training

Training data

A weighted instruction mix biased toward the two target skills (≈6.2K curated conversations):

Source	Purpose
HackerOne disclosed reports (public)	finding disposition + severity-triage signal
Curated bug-bounty methodology & triage heuristics	submit/kill discipline, validation gates, anti-patterns
Recon playbook / attack-surface examples	tech-stack to ranked vulnerability classes
Public detection-template patterns	low-false-positive authoring style
General-security instruction data (~13%)	rehearsal to limit catastrophic forgetting

No customer data, private program scope, credentials, or other non-public material is included in the training set. Only public or self-authored content was used.

Training procedure

QLoRA supervised fine-tuning, loss computed on assistant turns only.

Hyperparameter	Value
Quantisation	4-bit NF4 (base), BF16 compute
LoRA	r=32, α=32, dropout=0, all linear projections
Optimiser	paged AdamW 8-bit, weight decay 0.01
LR / schedule	2e-4, cosine, 3% warmup
Epochs / eff. batch	2 / 8 (micro-batch 1 × grad-accum 8)
Max sequence length	2,048
Hardware	1× NVIDIA RTX 4070 Ti SUPER (16 GB)
Frameworks	Unsloth · TRL 0.22 · Transformers 4.56 · PyTorch 2.9

Evaluation

v1 is scored with a deterministic, rubric-based held-out harness (no LLM judge): each item is decision- or rubric-scorable across triage (submit/kill accuracy), recon ranking (expected-class recall), and rubric categories (report/nuclei/payload/coding), comparing the tune against the Qwen2.5-Coder-14B base. The ship gate requires improvement on the two priority skills (triage, ranking) with no material regression on general coding (guarding against catastrophic forgetting). A full quantitative scorecard is published alongside v2; treat v1 as a capable assistant, not a benchmarked SOTA system.

Bias, risks, and limitations

Not a vulnerability discoverer. A 14B local model assists triage and prioritisation; it does not autonomously find or weaponise novel bugs, and can miss context a human or a larger system would catch.
Can be confidently wrong. It may over- or under-rate severity, hallucinate a CWE/CVE, or mis-scope a finding. Every output must be validated before acting or reporting.
Frozen knowledge. Trained on a static snapshot — it will not know the newest CVEs, techniques, or your current program scope. Pair with retrieval for facts.
Domain bias. Trained heavily on web-app / HackerOne-style findings; it is weaker on niche stacks, hardware, embedded, and non-web targets.
Dual-use. Security knowledge can be misused. The model is gated and authorization-scoped for this reason, but gating cannot prevent all misuse — see the Disclaimer.
Inherited base behaviour. Limitations and biases of Qwen2.5-Coder-14B-Instruct carry over.

Recommendations

Keep a human in the loop; use BountyHound as an assistive triage/ranking layer, not an oracle.
Validate every finding through your own impact gate before submitting; never paste output into a report unchecked.
Supplement with retrieval (CVE feeds, current scope) for anything time-sensitive.
Operate only within written authorization and your program's rules; follow responsible disclosure.

Disclaimer

This model is provided "as is" and "as available", without warranty of any kind, express or implied, including merchantability, fitness for a particular purpose, and non-infringement. By accessing or using BountyHound you acknowledge that you are solely responsible for your use of the model and its outputs, and you agree to indemnify and hold harmless the author and any affiliated parties from any claims, liabilities, damages, or costs arising from that use. Use is at your own risk and discretion. You are responsible for ensuring your use complies with all applicable laws, regulations, and the rules of any program or system you test. The author does not endorse or condone any unauthorized or unlawful use.

License and attribution

Weights are derived from Qwen/Qwen2.5-Coder-14B-Instruct and released under Apache-2.0, the base model's license.
Built with Unsloth and TRL.

Versions

v1 (this release) — core triage + recon co-pilot (≈6.2K-conversation mix).
v2 (in training) — adds a large, defanged CVE/CWE/vuln-class breadth layer derived from public exploit metadata; published with a head-to-head v1-vs-v2-vs-base scorecard.

Citation

@misc{bountyhound2026,
  title        = {BountyHound-Coder-14B: a gated bug-bounty triage and recon co-pilot},
  author       = {athulkrishnan},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/athulkrishnan/BountyHound-Coder-14B}},
  note         = {QLoRA SFT of Qwen2.5-Coder-14B-Instruct}
}

Downloads last month: -

Safetensors

Model size

15B params

Tensor type

BF16

Model tree for athulkrishnan/BountyHound-Coder-14B

Base model

Qwen/Qwen2.5-14B

Finetuned

Qwen/Qwen2.5-Coder-14B

Finetuned

Qwen/Qwen2.5-Coder-14B-Instruct

Finetuned

(108)

this model