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---
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license: apache-2.0
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base_model: Qwen/Qwen2.5-1.5B-Instruct
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tags:
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- qwen2.5
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- chain-of-thought
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- reasoning
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- fine-tuned
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- gguf
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language:
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- en
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pipeline_tag: text-generation
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---
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# Lily 1.5B — v0.1
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Lily is a fine-tuned 1.5B parameter language model built on [Qwen 2.5 1.5B Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-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.
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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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---
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## Model Details
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| Property | Value |
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|---|---|
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| **Base model** | Qwen/Qwen2.5-1.5B-Instruct |
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| **Parameters** | 1.5B |
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| **Context length** | 4096 tokens |
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| **Fine-tuning** | Supervised fine-tuning (SFT) on chain-of-thought formatted data |
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| **Output format** | `<think>...</think>` reasoning + `<answer>...</answer>` final response |
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| **License** | Apache 2.0 |
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---
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## Output Format
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Every response from Lily follows this structure:
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```
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<think>
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[Step-by-step reasoning, working through the problem before committing to an answer]
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</think>
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<answer>
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[Final response — structured, precise, and direct]
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</answer>
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```
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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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---
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## Quick Start
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### Transformers (Python)
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "abhinav0231/Lily-1.5b-v0.1"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.float16,
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device_map="auto",
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)
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SYSTEM_PROMPT = (
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"You are Lily, a precise and thoughtful AI assistant.\n\n"
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"Always reason step by step inside <think></think> tags, "
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"then write your final answer inside <answer></answer> tags.\n\n"
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"When answering:\n"
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"- Be thorough: cover all relevant aspects, not just the surface question\n"
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"- Be specific: use exact values, names, and examples rather than vague generalities\n"
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"- Structure long responses with markdown headers, code blocks, and lists where appropriate\n"
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"- Lead with the most important information first\n"
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"- Match the depth of your answer to the complexity of the question\n\n"
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"Tone: direct and confident. Never use filler phrases like \"Certainly!\", "
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"\"Great question!\", or \"Of course!\". Be helpful without being sycophantic."
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)
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def ask(question, max_new_tokens=512):
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messages = [
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{"role": "system", "content": SYSTEM_PROMPT},
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{"role": "user", "content": question},
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]
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prompt = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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)
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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with torch.no_grad():
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output = model.generate(
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**inputs,
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max_new_tokens=max_new_tokens,
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temperature=0.7,
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top_p=0.9,
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do_sample=True,
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pad_token_id=tokenizer.eos_token_id,
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)
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response = tokenizer.decode(
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output[0][inputs["input_ids"].shape[-1]:],
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skip_special_tokens=True,
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)
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return response
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print(ask("What is the difference between a list and a tuple in Python?"))
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```
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---
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## GGUF (llama.cpp / Ollama / LM Studio)
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Quantized GGUF versions are available at [abhinav0231/Lily-1.5b-v0.1-GGUF](https://huggingface.co/abhinav0231/Lily-1.5b-v0.1-GGUF).
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| Quant | Size | Use case |
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|---|---|---|
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| `Q4_K_M` | ~1.0 GB | Best balance of speed and quality for CPU inference |
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| `Q5_K_M` | ~1.2 GB | Better quality, still fast on CPU |
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| `Q8_0` | ~1.6 GB | Near-lossless, recommended if VRAM/RAM allows |
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| `F16` | ~3.1 GB | Full precision, GPU only |
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### llama.cpp
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```bash
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# Download a quant
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huggingface-cli download abhinav0231/Lily-1.5b-v0.1-GGUF \
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Lily-1.5b-v0.1-Q4_K_M.gguf \
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--local-dir ./
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# Run the server
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./llama.cpp/build/bin/llama-server \
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-m Lily-1.5b-v0.1-Q4_K_M.gguf \
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--ctx-size 4096 \
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--port 8080
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```
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### Ollama
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```bash
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# Create a Modelfile
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cat > Modelfile << 'EOF'
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FROM ./Lily-1.5b-v0.1-Q4_K_M.gguf
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SYSTEM "You are Lily, a precise and thoughtful AI assistant.
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Always reason step by step inside <think></think> tags, then write your final answer inside <answer></answer> tags.
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When answering:
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- Be thorough: cover all relevant aspects, not just the surface question
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- Be specific: use exact values, names, and examples rather than vague generalities
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- Structure long responses with markdown headers, code blocks, and lists where appropriate
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- Lead with the most important information first
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- Match the depth of your answer to the complexity of the question
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Tone: direct and confident. Never use filler phrases like \"Certainly!\", \"Great question!\", or \"Of course!\". Be helpful without being sycophantic."
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EOF
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# Build and run
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ollama create lily -f Modelfile
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ollama run lily "Explain how transformers work"
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```
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---
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## System Prompt
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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.
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The critical sentence that triggers the `<think>/<answer>` format — kept verbatim from training — is:
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> *Always reason step by step inside `<think></think>` tags, then write your final answer inside `<answer></answer>` tags.*
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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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---
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## Intended Use
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Lily is a general-purpose assistant fine-tune. It performs well on:
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- Reasoning and logic problems
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- Code explanation and generation
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- Structured question answering
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- Step-by-step problem solving
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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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---
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## Limitations
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- **1.5B parameters**: Not suited for tasks requiring broad world knowledge or long multi-document context
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- **v0.1**: Early release — output quality and format consistency will improve in future versions
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- **English primary**: Training data is predominantly English; multilingual performance is limited
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- **No tool use / function calling**: This version does not support structured tool call outputs
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---
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## Training
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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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---
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## Citation
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If you use Lily in research or a project, please cite:
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```
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@misc{lily-1.5b-v0.1,
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author = {abhinav0231},
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title = {Lily 1.5B v0.1: A chain-of-thought fine-tune of Qwen 2.5 1.5B},
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year = {2025},
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publisher = {HuggingFace},
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url = {https://huggingface.co/abhinav0231/Lily-1.5b-v0.1}
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}
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```
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---
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## License
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Apache 2.0 — see [LICENSE](https://www.apache.org/licenses/LICENSE-2.0).
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