Instructions to use kubernetes-bad/good-robot-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kubernetes-bad/good-robot-2 with Transformers:

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

pipe = pipeline("text-generation", model="kubernetes-bad/good-robot-2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kubernetes-bad/good-robot-2")
model = AutoModelForCausalLM.from_pretrained("kubernetes-bad/good-robot-2")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use kubernetes-bad/good-robot-2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kubernetes-bad/good-robot-2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kubernetes-bad/good-robot-2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kubernetes-bad/good-robot-2

SGLang

How to use kubernetes-bad/good-robot-2 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 "kubernetes-bad/good-robot-2" \
    --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": "kubernetes-bad/good-robot-2",
		"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 "kubernetes-bad/good-robot-2" \
        --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": "kubernetes-bad/good-robot-2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use kubernetes-bad/good-robot-2 with Docker Model Runner:
```
docker model run hf.co/kubernetes-bad/good-robot-2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Good Robot 2 🤖

The model "Good Robot" had one simple goal in mind: to be a good instruction-following model that doesn't talk like ChatGPT.

Built upon the Mistral 7b 0.2 base, this model aims to provide responses that are as human-like as possible, thanks to some DPO training using the (for now, private) minerva-ai/yes-robots-dpo dataset.

HuggingFaceH4/no-robots was used as the base for generating a custom dataset to create DPO pairs.

It should follow instructions and be generally as smart as a typical Mistral model - just not as soulless and full of GPT slop.

Changes from the original good-robot model:

Mistral 7b-0.2 base (32k native context, no SWA)
ChatML prompt format
Trained using GaLore method

Prompt Format:

ChatML

<|im_start|>system
System message
<|im_start|>user
User message<|im_end|>
<|im_start|>assistant

Credits:

Model made in collaboration with Gryphe.

Training Data:

HuggingFaceH4/no_robots
MinervaAI/yes-robots-dpo
private datasets with common GPTisms

Limitations:

While I did my best to minimize GPTisms, no model is perfect, and there may still be instances where the generated content has GPT's common phrases - I have a suspicion that's due to them being engrained into Mistral model itself.

License:

cc-by-nc-4.0

Downloads last month: 7

Safetensors

Model size

7B params

Tensor type

F16

Model tree for kubernetes-bad/good-robot-2

Quantizations

1 model

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