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license: apache-2.0
base_model: Qwen/Qwen3-1.7B
tags:
- pathi-ai
- pathi-labs
- pathi-lite-instruct-1.7B
- qwen3
- qwen
- causal-language-model
- text-generation
- conversational
- instruction-tuning
- lora
- peft
- fine-tuned
language:
- en
library_name: peft
pipeline_tag: text-generation
---
<div align="center">
# Pathi-Lite-Instruct-1.7B
**A lightweight, instruction-tuned language model by [Pathi Labs](https://www.pathilabs.com)**
[](https://www.pathilabs.com)
[](https://www.apache.org/licenses/LICENSE-2.0)
[](https://huggingface.co/Qwen/Qwen3-1.7B)
</div>
---
## Model Overview
**Pathi-Lite-Instruct-1.7B** is an instruction-tuned language model developed by **Pathi Labs LLP**, fine-tuned from [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) using **Low-Rank Adaptation (LoRA)**. It is designed to deliver efficient, high-quality instruction-following performance while remaining lightweight enough for accessible deployment.
This release is part of Pathi Labs' ongoing work in applied AI research and lightweight model development.
| | |
|---|---|
| **Developed by** | [Pathi Labs LLP](https://www.pathilabs.com) |
| **Model type** | Causal decoder-only transformer (instruction-tuned) |
| **Base model** | [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) |
| **Fine-tuning method** | LoRA (Low-Rank Adaptation) |
| **Language(s)** | English |
| **License** | Apache 2.0 |
| **Contact** | [Info@pathilabs.com](mailto:Info@pathilabs.com) |
---
### Intended Use
**Primary use cases:**
- Instruction following and general-purpose conversational assistance
- Lightweight deployment in resource-constrained environments
- A base for further fine-tuning or research experimentation
**Out-of-scope use:**
- High-stakes decision-making (medical, legal, financial) without human oversight
- Generation of harmful, misleading, or illegal content
- Use cases requiring guarantees of factual accuracy
### Limitations
- As a 1.7B-parameter model, it has a smaller knowledge and reasoning capacity than larger frontier models.
- May produce inaccurate, incomplete, or biased outputs; outputs should be reviewed before use in production.
- LoRA fine-tuning adapts behavior but does not remove limitations inherited from the base model.
---
## How to Use
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "pathilabs/Pathi-Lite-Instruct-1.7B"
# 1. Load Tokenizer and Merged Model
tokenizer = AutoTokenizer.from_pretrained(
model_id,
trust_remote_code=True
)
model = AutoModelForCausalLM.from_pretrained(
model_id,
dtype=torch.bfloat16, # bfloat16 matches native Qwen precision perfectly
device_map="auto",
trust_remote_code=True
)
# 2. Format inputs using the required Chat Template
messages = [
{"role": "user", "content": "What is artificial intelligence?"}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
inputs = tokenizer(
text,
return_tensors="pt"
).to(model.device)
# 3. Generate response with clean configuration parameters
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.8
)
# 4. Decode output cleanly
response = tokenizer.decode(
outputs[0][len(inputs.input_ids[0]):], # Cuts out the input prompt from printing twice
skip_special_tokens=True
)
print(response)
```
---
### Model Training Method
Fine-tuning was performed using **LoRA (Low-Rank Adaptation)**, a parameter-efficient technique that freezes the base model weights and trains small injected rank-decomposition matrices in select layers.
# Model Details
| Property | Value |
|---|---|
| Model name | Pathi-Lite-Instruct-1.7B |
| Organization | Pathi Labs LLP |
| Base model | Qwen/Qwen3-1.7B |
| Model family | Qwen3 |
| Model type | Causal Language Model |
| Parameter scale | ~1.7B |
| Fine-tuning method | LoRA / PEFT |
| Final release format | Merged model |
| Framework | Hugging Face Transformers |
| Serialization | Safetensors |
| Primary task | Text generation |
| Intended language | English |
| Developer | Pathi Labs LLP |
---
## Responsible Use
Pathi Labs encourages responsible deployment of this model. Users should:
- Evaluate outputs for accuracy and safety before use in production systems
- Avoid deploying the model in high-stakes domains without human review
- Respect the licensing terms of both this model and the base Qwen3-1.7B model
---
## Citation
If you use this model in your work, please cite:
```bibtex
@misc{pathilite2026,
title = {Pathi-Lite-Instruct-1.7B},
author = {Pathi Labs LLP},
year = {2026},
url = {https://huggingface.co/pathilabs/Pathi-Lite-Instruct-1.7B},
note = {Fine-tuned from Qwen/Qwen3-1.7B using LoRA}
}
```
---
## About Pathi Labs
**Pathi Labs LLP** is an AI research and development company building applied machine learning solutions.
- 🌐 Website: [www.pathilabs.com](https://www.pathilabs.com)
- 📧 Contact: [Info@pathilabs.com](mailto:Info@pathilabs.com)
---
## Acknowledgements
This model is built on top of [Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) by the Qwen Team, Alibaba Cloud. We thank the Qwen team for releasing their models openly.
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