Tiny-Purr-350M-merged 🐱

This is the full merged version of Tiny-Purr-350M. This model combines the LoRA adapter with the base LiquidAI/LFM2-350M model into a single, standalone model.

Note: If you want the lightweight LoRA adapter version instead, see purrgpt-community/Tiny-Purr-350M.

Terms of Service (Terms of Use / Terms and Conditions)

Model Description

This model is a fine-tuned version of LFM2-350M trained on the Tiny-Purr-2 dataset to generate conversational responses with a casual, friendly, and cat-themed personality.

Key characteristics:

• Lowercase, casual writing style
• Friendly and approachable tone
• Cat-themed expressions and personality
• Informative yet playful responses
• Bilingual capabilities (English and Chinese)

Model Type: Full merged model (ready to use without LoRA adapters)
Training Method: LoRA fine-tuning (now merged into weights)
Max sequence length: 1024 tokens

Intended Use

This model is designed for:

• Casual conversational AI applications
• Educational chatbots with personality
• Creative writing assistants
• Fun, engaging Q&A systems
• Quantization (GGUF, AWQ, GPTQ, etc.)

Not recommended for:

• Formal or professional communications
• Critical decision-making systems
• Medical, legal, or financial advice

Usage

Loading the Model

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "purrgpt-community/Tiny-Purr-350M-merged",
    device_map="auto",
    torch_dtype="auto"
)
tokenizer = AutoTokenizer.from_pretrained("purrgpt-community/Tiny-Purr-350M-merged")

Inference

prompt = "<|user|>\nWhat is notable about the ICSE 2002 conference?\n<|assistant|>\n"

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=150,
    temperature=0.7,
    top_p=0.9,
    do_sample=True
)

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

If you want a system prompt use this:

prompt = "<|system|>\nYou are Tiny-Purr\n<|user|>\nWhat is notable about the ICSE 2002 conference?\n<|assistant|>\n"

Example Output

<|user|>
What is notable about the ICSE 2002 conference?
<|assistant|>
oh, the 2002 icse conference, you say? sounds like a lot of stuff, user. *purrrr*.

the icse conference was a big deal. they brought together top scientists and 
researchers from all over the world to discuss cutting-edge topics in science 
and technology. the 2002 one was particularly notable because it focused on 
areas like nanotechnology, artificial intelligence, and bioengineering. it 
really opened up new avenues for research and collaboration.

did you know they also got a lot of international media coverage? that's 
something. it's pretty much like the world's biggest science fair, but with 
even more top-tier scientists.

Prompt Format

The model expects prompts in the following format:

<|user|>
[Your question or prompt here]
<|assistant|>

The model will then generate a response following the assistant tag.

Training Details

This model was created by fine-tuning LFM2-350M using LoRA, then merging the adapter weights into the base model.

Training Data

• Dataset: purrgpt-community/The-Tiny-Purr-2
• Content: Conversational Q&A pairs covering academic conferences, university activities, gaming industry news, technology topics, and general knowledge
• Languages: English and Chinese

LoRA Configuration (before merging)

LoraConfig(
    r=16,
    lora_alpha=32,
    target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM"
)

Training Hyperparameters

• Learning rate: 2e-4
• Batch size: 4 (per device)
• Gradient accumulation steps: 4
• Effective batch size: 16
• Optimizer: AdamW
• LR scheduler: Cosine
• Warmup ratio: 0.03
• Precision: BFloat16
• Epochs: 3
• Trainable parameters: 491,520 (0.14% of total)

Hardware

• GPU: NVIDIA P100 (16GB VRAM)
• Training time: ~25 minutes

Limitations

• Casual tone only: Not suitable for formal or professional contexts
• Factual accuracy: May produce creative or incorrect information, especially for recent events
• Bias: Inherits biases from both the base model and training dataset
• Language mixing: May occasionally mix English and Chinese unexpectedly
• Context length: Limited to 1024 tokens per conversation turn

Ethical Considerations

• This model should not be used for impersonation or generating misleading content
• Responses should be verified for accuracy in critical applications
• The casual, playful tone may not be appropriate for all audiences
• Users should be aware they are interacting with an AI system

Versions

• LoRA adapter: purrgpt-community/Tiny-Purr-350M - Lightweight adapter (recommended if you already have the base model)
• Full merged model: purrgpt-community/Tiny-Purr-350M-merged - This repo (recommended for quantization or standalone use)

Citation

If you use this model, please cite:

@misc{lfm2-tiny-purr-2024,
  title={Tiny-Purr-350M: A Conversational Fine-tune of LFM2},
  author={purrgpt-community},
  year={2024},
  publisher={HuggingFace},
  howpublished={\url{https://huggingface.co/purrgpt-community/Tiny-Purr-350M-merged}}
}

Base Model Citation

@misc{liquid2024lfm,
  title={Liquid Foundation Models},
  author={Liquid AI Team},
  year={2024},
  publisher={HuggingFace},
  howpublished={\url{https://huggingface.co/LiquidAI/LFM2-350M}}
}

Acknowledgments

• Base Model: LiquidAI/LFM2-350M
• Training Dataset: purrgpt-community/The-Tiny-Purr-2
• Fine-tuning Framework: Hugging Face PEFT (LoRA)
• LoRA Adapter: purrgpt-community/Tiny-Purr-350M

License

This model is released under the Apache 2.0 license, inheriting from the base LFM2-350M model.

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Made with 🐱 and LoRA, now fully merged for your convenience!