Pathi-Lite-Instruct-1.7B

A lightweight, instruction-tuned language model by Pathi Labs

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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 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
Model type	Causal decoder-only transformer (instruction-tuned)
Base model	Qwen/Qwen3-1.7B
Fine-tuning method	LoRA (Low-Rank Adaptation)
Language(s)	English
License	Apache 2.0
Contact	Info@pathilabs.com

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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.

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How to Use

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)

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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

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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

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Citation

If you use this model in your work, please cite:

@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}
}

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About Pathi Labs

Pathi Labs LLP is an AI research and development company building applied machine learning solutions.

• 🌐 Website: www.pathilabs.com
• 📧 Contact: Info@pathilabs.com

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Acknowledgements

This model is built on top of Qwen3-1.7B by the Qwen Team, Alibaba Cloud. We thank the Qwen team for releasing their models openly.