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.

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