Instructions to use haphazardlyinc/Andy-Feather-V2-700m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use haphazardlyinc/Andy-Feather-V2-700m with PEFT:
```
Task type is invalid.
```

How to use haphazardlyinc/Andy-Feather-V2-700m with Transformers:

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

pipe = pipeline("text-generation", model="haphazardlyinc/Andy-Feather-V2-700m")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("haphazardlyinc/Andy-Feather-V2-700m")
model = AutoModelForCausalLM.from_pretrained("haphazardlyinc/Andy-Feather-V2-700m")
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 haphazardlyinc/Andy-Feather-V2-700m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "haphazardlyinc/Andy-Feather-V2-700m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "haphazardlyinc/Andy-Feather-V2-700m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/haphazardlyinc/Andy-Feather-V2-700m

SGLang

How to use haphazardlyinc/Andy-Feather-V2-700m 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 "haphazardlyinc/Andy-Feather-V2-700m" \
    --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": "haphazardlyinc/Andy-Feather-V2-700m",
		"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 "haphazardlyinc/Andy-Feather-V2-700m" \
        --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": "haphazardlyinc/Andy-Feather-V2-700m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use haphazardlyinc/Andy-Feather-V2-700m 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 haphazardlyinc/Andy-Feather-V2-700m 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 haphazardlyinc/Andy-Feather-V2-700m to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for haphazardlyinc/Andy-Feather-V2-700m to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="haphazardlyinc/Andy-Feather-V2-700m",
    max_seq_length=2048,
)

Docker Model Runner
How to use haphazardlyinc/Andy-Feather-V2-700m with Docker Model Runner:
```
docker model run hf.co/haphazardlyinc/Andy-Feather-V2-700m
```

Model Card for Andy Feather 700M

⚠️⚠️⚠️IMPORTANT⚠️⚠️⚠️ In its current state, this model DOES NOT perform very well with Mindcraft and can only do very rudimentary tasks. It is a HUGE step up from V1, but still has absolutely ABYSMAL performance.

This model is a fine-tuned LoRA adapter built on top of LiquidAI/LFM2-700M.
It is designed for CPU inference or those who are GPU poor, requiring sub 1gb of memory to load the model at Q8 precision. The model is NOT compatible with Ollama, it is highly recommended to use LMStudio instead. Here is an example Mindcraft profile:

{
    "name": "andy",
    "model": {
        "api": "openai",
        "url": "http://localhost:1234/v1",
        "model": "Andy-Feather-V2-700m"
    },
    "embedding": {
        "api": "openai",
        "url": "http://localhost:1234/v1",
        "model": "text-embedding-nomic-embed-text-v1.5"
    }
}

And an example Mindcraft keys.json:

{
    "OPENAI_API_KEY": "http://localhost:1234/v1",
    "OPENAI_ORG_ID": "",
    "GEMINI_API_KEY": "",
    "ANTHROPIC_API_KEY": "",
    "REPLICATE_API_KEY": "",
    "GROQCLOUD_API_KEY": "",
    "HUGGINGFACE_API_KEY": "",
    "QWEN_API_KEY": "",
    "XAI_API_KEY": "",
    "MISTRAL_API_KEY": "",
    "DEEPSEEK_API_KEY": "",
    "GHLF_API_KEY": "",
    "HYPERBOLIC_API_KEY": "",
    "NOVITA_API_KEY": "",
    "OPENROUTER_API_KEY": "",
    "CEREBRAS_API_KEY": "",
    "MERCURY_API_KEY":""
}

Training Data

This model was trained on the following datasets:

Sweaterdog/Andy-base-2
Sweaterdog/Andy-4-base
Sweaterdog/Andy-4-FT

Dataset License

The training data is subject to the Andy 1.0 License

This work uses data and models created by @Sweaterdog.

@misc{vonwerra2022trl,
  title        = {{TRL: Transformer Reinforcement Learning}},
  author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
  year         = {2020},
  journal      = {GitHub repository},
  publisher    = {GitHub},
  howpublished = {\url{https://github.com/huggingface/trl}}
}

@misc{liquidai_lfm2_700m,
  title        = {LFM2-700M},
  author       = {Liquid AI},
  year         = {2024},
  howpublished = {\url{https://huggingface.co/LiquidAI/LFM2-700M}}
}