Lily-1.5b-v0.1-GGUF

Lily 1.5B — v0.1

Lily is a fine-tuned 1.5B parameter language model built on Qwen 2.5 1.5B Instruct. It is trained to reason explicitly before answering — every response includes a visible thinking step inside <think> tags, followed by the final answer inside <answer> tags.

The model is optimized for precision and structured output. It stays direct, avoids filler phrases, and scales response depth to the complexity of the question.

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Model Details

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Benchmarks

Benchmark Scores: Lily absolutely beats Gemma-2B and Llama 3.2 1B on every benchmark, while beating it's own base model Qwen-2.5-1.5B on ARC Challenge and beating SmolLM2-1.7B on Math.

*Note: Qwen's published GSM8K score uses their internal evaluation pipeline while the Lily was evaluated on lm-eval harness.

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Output Format

Every response from Lily follows this structure:

<think>
[Step-by-step reasoning, working through the problem before committing to an answer]
</think>
<answer>
[Final response — structured, precise, and direct]
</answer>

The <think> block is Lily's scratchpad — it plans, evaluates, and drafts before producing the answer. This makes the model's reasoning transparent and auditable.

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Quick Start

Transformers (Python)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "abhinav0231/Lily-1.5b-v0.1"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
)

SYSTEM_PROMPT = (
    "You are Lily, a precise and thoughtful AI assistant.\n\n"
    "Always reason step by step inside <think></think> tags, "
    "then write your final answer inside <answer></answer> tags.\n\n"
    "When answering:\n"
    "- Be thorough: cover all relevant aspects, not just the surface question\n"
    "- Be specific: use exact values, names, and examples rather than vague generalities\n"
    "- Structure long responses with markdown headers, code blocks, and lists where appropriate\n"
    "- Lead with the most important information first\n"
    "- Match the depth of your answer to the complexity of the question\n\n"
    "Tone: direct and confident. Never use filler phrases like \"Certainly!\", "
    "\"Great question!\", or \"Of course!\". Be helpful without being sycophantic."
)

def ask(question, max_new_tokens=512):
    messages = [
        {"role": "system", "content": SYSTEM_PROMPT},
        {"role": "user",   "content": question},
    ]
    prompt = tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True,
    )
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    with torch.no_grad():
        output = model.generate(
            **inputs,
            max_new_tokens=max_new_tokens,
            temperature=0.7,
            top_p=0.9,
            do_sample=True,
            pad_token_id=tokenizer.eos_token_id,
        )
    response = tokenizer.decode(
        output[0][inputs["input_ids"].shape[-1]:],
        skip_special_tokens=True,
    )
    return response

print(ask("What is the difference between a list and a tuple in Python?"))

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GGUF (llama.cpp / Ollama / LM Studio)

Quantized GGUF versions are available at abhinav0231/Lily-1.5b-v0.1-GGUF.

llama.cpp

# Download a quant
huggingface-cli download abhinav0231/Lily-1.5b-v0.1-GGUF \
    Lily-1.5b-v0.1-Q4_K_M.gguf \
    --local-dir ./

# Run the server
./llama.cpp/build/bin/llama-server \
    -m Lily-1.5b-v0.1-Q4_K_M.gguf \
    --ctx-size 4096 \
    --port 8080

Ollama

# Create a Modelfile
cat > Modelfile << 'EOF'
FROM ./Lily-1.5b-v0.1-Q4_K_M.gguf
SYSTEM "You are Lily, a precise and thoughtful AI assistant.

Always reason step by step inside <think></think> tags, then write your final answer inside <answer></answer> tags.

When answering:
- Be thorough: cover all relevant aspects, not just the surface question
- Be specific: use exact values, names, and examples rather than vague generalities
- Structure long responses with markdown headers, code blocks, and lists where appropriate
- Lead with the most important information first
- Match the depth of your answer to the complexity of the question

Tone: direct and confident. Never use filler phrases like \"Certainly!\", \"Great question!\", or \"Of course!\". Be helpful without being sycophantic."
EOF

# Build and run
ollama create lily -f Modelfile
ollama run lily "Explain how transformers work"

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System Prompt

The system prompt below is embedded in the model's chat template and applied automatically when using apply_chat_template. You do not need to set it manually if using the Transformers pipeline — it is already the default.

The critical sentence that triggers the <think>/<answer> format — kept verbatim from training — is:

Always reason step by step inside <think></think> tags, then write your final answer inside <answer></answer> tags.

The rest of the system prompt shapes tone and response quality and can be overridden by passing a custom system message.

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Intended Use

Lily is a general-purpose assistant fine-tune. It performs well on:

• Reasoning and logic problems
• Code explanation and generation
• Structured question answering
• Step-by-step problem solving

The explicit <think> step makes it especially useful in applications where reasoning transparency matters — grading, debugging, tutoring, or any workflow where you need to see why the model gave a particular answer, not just the answer itself.

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Limitations

• 1.5B parameters: Not suited for tasks requiring broad world knowledge or long multi-document context
• v0.1: Early release — output quality and format consistency will improve in future versions
• English primary: Training data is predominantly English; multilingual performance is limited
• No tool use / function calling: This version does not support structured tool call outputs

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Training

Fine-tuned from Qwen/Qwen2.5-1.5B-Instruct using supervised fine-tuning on a dataset of chain-of-thought formatted examples. Each training example uses the <think>/<answer> output structure. Training was performed on a single T4 GPU via Google Colab.

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Citation

If you use Lily in research or a project, please cite:

@misc{lily-1.5b-v0.1,
  author    = {abhinav0231},
  title     = {Lily 1.5B v0.1: A chain-of-thought fine-tune of Qwen 2.5 1.5B},
  year      = {2025},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/abhinav0231/Lily-1.5b-v0.1}
}

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License

Apache 2.0 — see LICENSE.