Instructions to use DeepHat/DeepHat-V1-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DeepHat/DeepHat-V1-7B with Transformers:

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

pipe = pipeline("text-generation", model="DeepHat/DeepHat-V1-7B")
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("DeepHat/DeepHat-V1-7B")
model = AutoModelForCausalLM.from_pretrained("DeepHat/DeepHat-V1-7B", 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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DeepHat/DeepHat-V1-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DeepHat/DeepHat-V1-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DeepHat/DeepHat-V1-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DeepHat/DeepHat-V1-7B

SGLang

How to use DeepHat/DeepHat-V1-7B 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 "DeepHat/DeepHat-V1-7B" \
    --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": "DeepHat/DeepHat-V1-7B",
		"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 "DeepHat/DeepHat-V1-7B" \
        --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": "DeepHat/DeepHat-V1-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use DeepHat/DeepHat-V1-7B with Docker Model Runner:
```
docker model run hf.co/DeepHat/DeepHat-V1-7B
```

devon-kindo commited on Jul 21, 2025

Commit

3d0a4aa

verified ·

1 Parent(s): 7665282

Update README.md

Browse files

Files changed (1) hide show

README.md +8 -8

README.md CHANGED Viewed

@@ -14,13 +14,13 @@ tags:
 <br>
-# WhiteRabbitNeo
 <br>
-![WhiteRabbitNeo](https://huggingface.co/WhiteRabbitNeo/WhiteRabbitNeo-V3-7B/resolve/main/whiterabbitneo-logo-defcon.png)
-WhiteRabbitNeo is a model series that can be used for offensive and defensive cybersecurity. Access at [whiterabbitneo.com](https://www.whiterabbitneo.com/) or go to [Kindo.ai](https://www.kindo.ai/) to create agents.
 # Community
@@ -29,7 +29,7 @@ Join us on [Discord](https://discord.gg/8Ynkrcbk92)
 # Technical Overview
-WhiteRabbitNeo is a finetune of [Qwen2.5-Coder-7B](https://huggingface.co/Qwen/Qwen2.5-Coder-7B/), and inherits the following features:
 - Type: Causal Language Models
 - Training Stage: Pretraining & Post-training
 - Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
@@ -57,7 +57,7 @@ Here provides a code snippet with `apply_chat_template` to show you how to load
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
-model_name = "WhiteRabbitNeo/WhiteRabbitNeo-V3-7B"
 model = AutoModelForCausalLM.from_pretrained(
     model_name,
@@ -68,7 +68,7 @@ tokenizer = AutoTokenizer.from_pretrained(model_name)
 prompt = "write a quick sort algorithm."
 messages = [
-    {"role": "system", "content": "You are WhiteRabbitNeo, created by Kindo.ai. You are a helpful assistant that is an expert in Cybersecurity and DevOps."},
     {"role": "user", "content": prompt}
 ]
 text = tokenizer.apply_chat_template(
@@ -108,9 +108,9 @@ For supported frameworks, you could add the following to `config.json` to enable
 # License
-Apache-2.0 + WhiteRabbitNeo Extended Version
-## WhiteRabbitNeo Extension to Apache-2.0 Licence: Usage Restrictions
 ```
 You agree not to use the Model or Derivatives of the Model:

 <br>
+# DeepHat
 <br>
+![DeepHat](https://huggingface.co/WhiteRabbitNeo/WhiteRabbitNeo-V3-7B/resolve/main/whiterabbitneo-logo-defcon.png)
+DeepHat is a model series that can be used for offensive and defensive cybersecurity. Access at [whiterabbitneo.com](https://www.whiterabbitneo.com/) or go to [Kindo.ai](https://www.kindo.ai/) to create agents.
 # Community
 # Technical Overview
+DeepHat is a finetune of [Qwen2.5-Coder-7B](https://huggingface.co/Qwen/Qwen2.5-Coder-7B/), and inherits the following features:
 - Type: Causal Language Models
 - Training Stage: Pretraining & Post-training
 - Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
+model_name = "DeepHat/DeepHat-V1-7B"
 model = AutoModelForCausalLM.from_pretrained(
     model_name,
 prompt = "write a quick sort algorithm."
 messages = [
+    {"role": "system", "content": "You are DeepHat, created by Kindo.ai. You are a helpful assistant that is an expert in Cybersecurity and DevOps."},
     {"role": "user", "content": prompt}
 ]
 text = tokenizer.apply_chat_template(
 # License
+Apache-2.0 + DeepHat Extended Version
+## DeepHat Extension to Apache-2.0 Licence: Usage Restrictions
 ```
 You agree not to use the Model or Derivatives of the Model: