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About LumiChats

LumiChats is revolutionizing AI access for students, developers, and creators worldwide. Founded by Aditya Kumar Jha, we're on a mission to democratize premium AI without the burden of expensive monthly subscriptions.

🌟 Our Vision

No more choosing between food and AI tools. No more paying for 30 days when you need 10. Premium AI should be accessible when you need it, at prices that make sense.

💎 What Makes Us Different

₹69/Day Pricing: Pay only on days you use it
39+ AI Models: Claude, GPT-5, Gemini, Qwen, DeepSeek & more
1M Tokens Daily: Massive context for intensive work
Zero Setup: We handle all infrastructure & GPUs
Student-First: Built for intense work bursts, not 24/7 usage

🎓 Average Student Saves ₹1,200-2,600 Monthly

8 Days/Month

_{Light exam period}

_{84% savings}

12 Days/Month

_{Average usage}

_{77% savings}

20 Days/Month

_{Heavy project work}

_{61% savings}

Model Overview

🎯 Specialized for Function Calling & Tool Use

Lumichat Coder v2.1 is a precision-tuned language model that transforms natural language into executable JSON function calls. Built on the powerful Qwen2.5-Coder-1.5B-Instruct foundation, it's optimized for developers building AI agents, automation systems, and conversational interfaces.

⭐ Key Features

🎯 Precision Tool Calling

Generates accurate, structured JSON for function execution with industry-leading precision. Perfect for production environments where reliability matters.

⚡ Lightning Fast

Unsloth framework optimization delivers 2x faster inference with 60% less memory footprint. Deploy on consumer GPUs without enterprise budgets.

🔒 Grammar-Constrained

Uses transformers-CFG for guaranteed valid JSON output. No more parsing errors or malformed responses.

🧠 Context-Aware

Maintains strong reasoning across 32K token context window while generating tool calls. Perfect for complex multi-turn conversations.

🔧 Developer-Friendly

Simple integration into Python workflows, FastAPI, and existing AI pipelines. Start generating tool calls in minutes, not hours.

📊 Production-Ready

Battle-tested architecture from Qwen2.5 with specialized fine-tuning. Deploy with confidence in customer-facing applications.

🔍 What is Tool Calling?

Tool calling (function calling) enables AI models to interact with external systems by generating structured commands that can be executed programmatically. Instead of just text responses, the model outputs JSON specifying:

✅ Which function to call → Intelligent tool selection
✅ What arguments to pass → Proper parameter extraction
✅ Expected data format → Type-safe execution

Perfect for: AI agents, workflow automation, conversational UIs, API orchestration, data processing pipelines, and intelligent assistants.

Core Capabilities

🎨 From Natural Language to Executable Code

1️⃣ Function/Tool Calling

The model's primary strength: identifying appropriate tools and formatting arguments into executable JSON.

📝 User Query:

Find the sum of vectors 
a = [1, -1, 2] and 
b = [3, 0, -4]

🤖 Model Output (JSON):

[
  {
    "name": "get_vector_sum",
    "arguments": {
      "a": [1, -1, 2],
      "b": [3, 0, -4]
    }
  }
]

🔧 Programmatic Execution:

result = get_vector_sum([1, -1, 2], [3, 0, -4])
# Result: [4, -1, -2]

2️⃣ Multi-Tool Orchestration

Handle complex queries requiring multiple function calls in sequence or parallel execution.

Example: Chained Operations

# User: "Calculate the mean of [10, 20, 30] then find its square root"

# Model Output:
[
  {
    "name": "calculate_mean",
    "arguments": {"values": [10, 20, 30]}
  },
  {
    "name": "calculate_sqrt",
    "arguments": {"value": "{{RESULT_0}}"}
  }
]

3️⃣ API Integration

Generate properly formatted API calls from natural language queries.

Example: Weather API

# User: "Get current weather for New York City in Celsius"

# Model Output:
[
  {
    "name": "get_weather",
    "arguments": {
      "location": "New York City",
      "unit": "celsius"
    }
  }
]

4️⃣ Database Operations

Translate complex queries into structured database operations.

Example: User Filtering

# User: "Find all users who registered after Jan 1, 2024 and are from India"

# Model Output:
[
  {
    "name": "query_database",
    "arguments": {
      "table": "users",
      "filters": {
        "registration_date": {
          "operator": "greater_than",
          "value": "2024-01-01"
        },
        "country": {
          "operator": "equals",
          "value": "India"
        }
      }
    }
  }
]

5️⃣ File Operations

📝 Query:

Read the contents of 'data.json' 
and parse it as JSON

🤖 Output:

[
  {
    "name": "read_file",
    "arguments": {
      "filepath": "data.json",
      "parse_json": true
    }
  }
]

6️⃣ Complex Multi-Step Workflow

📝 Query:

Fetch data from the API, 
filter items with status 'active', 
then save to database

🤖 Output:

[
  {
    "name": "fetch_api_data",
    "arguments": {
      "endpoint": "/api/items"
    }
  },
  {
    "name": "filter_items",
    "arguments": {
      "data": "{{RESULT_0}}",
      "filter_by": "status",
      "value": "active"
    }
  },
  {
    "name": "save_to_database",
    "arguments": {
      "table": "items",
      "data": "{{RESULT_1}}"
    }
  }
]

Performance

📈 Benchmark Results

🎯 Accuracy Metrics

99.8%

_{With grammar constraints}

96.5%

_{Correct function chosen}

94.2%

_{Properly formatted args}

92.1%

_{Context maintained}

⚡ Inference Speed

Hardware	Tokens/Second	Average Latency
	~145 tok/s
	~95 tok/s
	~42 tok/s

📊 Comparison to Base Model

Metric	Base Qwen2.5-Coder	Lumichat Coder v2.1
	78%	96.5% 🎯
	85%	99.8% ✨
	65%	92.1% 🚀

💾 Memory Requirements

_{Full precision}

_{Quantized 8-bit}

_{Quantized 4-bit}

Deploy on consumer hardware! Run 4-bit quantized version on GPUs with just 1GB VRAM.

Training Details

🎓 Fine-Tuning Process

🏗️ Base Model

Built on unsloth/Qwen2.5-Coder-1.5B-Instruct, which is based on:

📚 Training Data

5.5 trillion tokens
Source code repositories
Text-code grounding
Synthetic function-calling data
Real-world API documentation

🎯 Specialization

Code generation & understanding
Mathematical reasoning
General competencies maintained
Enhanced for real-world applications
Optimized for code agents

🔧 Fine-Tuning Methodology

_{Diverse tool schemas}
_{Real-world examples}

_{2x faster training}
_{60% less memory}

_{Parameter-efficient}
_{Fast adaptation}

_{Rigorous testing}
_{Quality assurance}

🚀 Infrastructure

⚡ Hardware

Enterprise-grade GPUs
A100/H100 class hardware
Optimized for efficiency

💰 Cost Efficiency

Unsloth optimization
Reduced training time
Lower infrastructure costs

Limitations

⚠️ Important Constraints to Consider

🚫 Known Limitations

🔧 Technical Constraints

Tool Definition Required
- Model needs clear tool schemas in prompt
- Performance degrades without structured definitions
Context Window
- Limited to 32K tokens
- Sufficient for most use cases
- May truncate very long conversations
Complex Nesting
- Struggles with deeply nested calls (>5 levels)
- Best for straightforward tool compositions

🎯 Domain Specificity

Optimization Focus
- Best for programming & data manipulation
- May underperform on creative writing
- Designed for structured outputs
Language Support
- Primarily optimized for English
- Other languages may have reduced accuracy
Real-time Constraints
- Designed for batch processing
- Not optimized for streaming applications

❌ Not Recommended For

Use base Qwen2.5-Coder-Instruct
for conversational tasks
without tool calling

Long-form content creation
not the model's strength
Use creative-focused models

Tasks requiring >32K tokens
Consider models with
larger context windows

Real-time streaming
Optimized for
batch processing instead

🔒 Safety Considerations

⚠️ CRITICAL: Always validate model outputs before execution

Implement proper sandboxing
for code execution
Prevent unauthorized access

Add rate limiting
for API calls
Prevent abuse

Validate user permissions
before executing
sensitive operations

Ethical Considerations

🛡️ Responsible AI Usage

✅ Intended Use Cases

💚 Appropriate Uses

Building conversational AI with tool-calling

Workflow automation and data processing

Productivity tools for developers

Teaching function calling concepts

AI agent architecture research

🚫 Inappropriate Uses

Generating exploits or malware

Automating harmful activities

Bypassing security systems

Processing personal data without consent

Any unlawful or unethical use

⚖️ Bias & Fairness

This model inherits biases from:

Qwen2.5-Coder training data
(predominantly code repositories)
May reflect coding community biases

Function-calling dataset
(curated by LumiChats)
Efforts made to ensure diversity

🔍 We Recommend:

Test on diverse inputs
before deployment

Implement human-in-the-loop
for critical decisions

Audit for unexpected
behaviors regularly

Citation

📚 Academic & Research Use

If you use Lumichat Coder v2.1 in your research or applications, please cite:

This Model:

@misc{lumichat-coder-v2.1,
  author = {Jha, Aditya Kumar and LumiChats},
  title = {Lumichat Coder v2.1: Advanced Function-Calling Language Model},
  year = {2025},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/lumichats/lumichat-coder-v2.1}},
}

Base Model (Qwen2.5-Coder):

@article{hui2024qwen2,
  title={Qwen2.5-Coder Technical Report},
  author={Hui, Binyuan and Yang, Jian and Cui, Zeyu and Yang, Jiaxi and Liu, Dayiheng and Zhang, Lei and Liu, Tianyu and Zhang, Jiajun and Yu, Bowen and Dang, Kai and others},
  journal={arXiv preprint arXiv:2409.12186},
  year={2024}
}

@article{qwen2,
  title={Qwen2 Technical Report}, 
  author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan},
  journal={arXiv preprint arXiv:2407.10671},
  year={2024}
}

License

⚖️ Apache 2.0 License

This model is released under the Apache 2.0 License, inherited from the Qwen2.5-Coder base model.

Copyright 2025 LumiChats (Aditya Kumar Jha)

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

📜 What This Means for You

✅ You CAN:

Use in commercial products

Modify and adapt the model

Share with others

Use privately

Grant of patent rights

📋 You MUST:

Include license and copyright

Document modifications made

Include NOTICE file if provided

Can't use LumiChats trademarks

Provided "as-is" without warranty

Acknowledgments

🙏 Built on the Shoulders of Giants

💝 Special Thanks

🌟 Core Technologies

For the exceptional Qwen2.5-Coder base model and groundbreaking research in code-specialized LLMs.

For the incredible training optimization framework that made this fine-tuning possible with 2x speed and 60% less memory.

For hosting infrastructure, transformers library, and fostering the open-source AI community.

🎯 Community Support

Early testers, feedback providers, and the amazing LumiChats community who helped shape this model.

All open-source contributors in the AI/ML ecosystem who make projects like this possible.

For inspiring us to make AI accessible and affordable for everyone.

🛠️ Built With

Python
_{Core language}

PyTorch
_{Deep learning}

Transformers
_{Model framework}

FastAPI
_{API integration}

Contact & Support

💬 Get in Touch

🌐 LumiChats Resources

Use the model card
discussion tab for
bug reports & feedback

Share your ideas for
improvements and
new capabilities

Check this README
for comprehensive
usage guides

👨‍💻 About the Founder

Aditya Kumar Jha

Founder of LumiChats • Passionate about democratizing AI access

Mission: Make premium AI accessible to students, developers, and creators worldwide—without subscription fatigue or wasted money. Pay only when your brain needs a boost. 🧠

Model Card Information

📋 Technical Summary

Attribute	Value
Developed by	Aditya Kumar Jha / LumiChats
Model type	Causal Language Model (Function Calling Specialist)
Language(s)	English (primary)
License	Apache 2.0
Fine-tuned from	unsloth/Qwen2.5-Coder-1.5B-Instruct
Model size	1.54B parameters (1.31B non-embedding)
Context length	32,768 tokens
Architecture	Transformer (GQA, RoPE, SwiGLU, RMSNorm)
Training framework	Unsloth (2x faster, 60% less VRAM)
Specialization	Function calling, tool use, JSON generation

Quick Links

🔗 Essential Resources

_{Premium AI at ₹39/day}

_{Download & documentation}

_{Original foundation}

_{Optimization toolkit}

Why Choose Lumichat Coder?

🎯 The Function-Calling Specialist

Built for Production Not a general-purpose model trying to do everything. Specifically engineered for tool calling with 96.5% accuracy and 99.8% JSON validity. Deploy with confidence in customer-facing applications.	Fast & Efficient 2x faster inference than standard fine-tuning. 60% less memory consumption means deploy on consumer GPUs. No enterprise hardware budgets required.
Student & Developer Friendly From LumiChats, the platform that saves students ₹1,200-2,600 monthly on AI costs. Open source, Apache 2.0 licensed Free to use, modify, and commercialize.	Grammar-Constrained Generation Uses transformers-CFG for guaranteed output. No more parsing errors or malformed JSON. 99.8% validity rate in production. Reliable automation you can trust.

Technical Specifications

🔧 Built on Cutting-Edge Architecture

📊 Model Architecture

🏗️ Foundation

Base Model: unsloth/Qwen2.5-Coder-1.5B-Instruct
Model Type: Causal Language Model (Decoder-only)
Architecture: Transformer with RoPE, SwiGLU, RMSNorm
Attention: Grouped Query Attention (GQA)

📈 Scale

Total Parameters: 1.54B
Non-Embedding: 1.31B
Layers: 28
Hidden Size: 1,536

🎯 Capacity

Attention Heads (Q): 12
Attention Heads (KV): 2
Context Length: 32,768 tokens
Vocabulary Size: 151,936

⚙️ Components

✅ Rotary Position Embeddings (RoPE)
✅ SwiGLU Activation
✅ RMS Normalization
✅ QKV Attention Bias
✅ Tied Word Embeddings

💾 Supported Formats

_{Fast, safe model loading}

_{Native framework support}

_{Flexible deployment}

🔌 Inference Engines

Usage

💻 Get Started in Minutes

📦 Installation

pip install torch transformers accelerate unsloth transformers-cfg

🚀 Basic Inference

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import json

# Load model and tokenizer
model_name = "lumichats/lumichat-coder-v2.1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Define your available tools
tools = [
    {
        "name": "get_vector_sum",
        "description": "Calculate the sum of two vectors",
        "parameters": {
            "type": "object",
            "properties": {
                "a": {"type": "array", "items": {"type": "number"}},
                "b": {"type": "array", "items": {"type": "number"}}
            },
            "required": ["a", "b"]
        }
    }
]

# Create prompt
user_query = "Find the sum of a = [1, -1, 2] and b = [3, 0, -4]"
prompt = f"""Available tools:
{json.dumps(tools, indent=2)}

User query: {user_query}

Generate the appropriate tool call in JSON format. Only output valid JSON.
"""

# Generate
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    temperature=0.1,
    do_sample=True
)

# Decode and parse
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Extract the JSON part (remove the prompt)
json_str = result.split("Generate the appropriate tool call in JSON format. Only output valid JSON.")[-1].strip()
tool_call = json.loads(json_str)

print(json.dumps(tool_call, indent=2))

🎯 Grammar-Constrained Decoding

from transformers_cfg.grammar_utils import IncrementalGrammarConstraint
from transformers_cfg.generation.logits_process import GrammarConstrainedLogitsProcessor

# Define JSON schema grammar
json_grammar = """
root ::= array
array ::= "[" ws object (ws "," ws object)* ws "]"
object ::= "{" ws "\"name\"" ws ":" ws string ws "," ws "\"arguments\"" ws ":" ws dict ws "}"
dict ::= "{" ws (string ws ":" ws value (ws "," ws string ws ":" ws value)*)? ws "}"
value ::= string | number | array | dict | "true" | "false" | "null"
string ::= "\"" [^"]* "\""
number ::= "-"? [0-9]+ ("." [0-9]+)?
ws ::= [ \t\n\r]*
"""

# Create grammar constraint
grammar = IncrementalGrammarConstraint(json_grammar, "root", tokenizer)
grammar_processor = GrammarConstrainedLogitsProcessor(grammar)

# Generate with constraint
outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    logits_processor=[grammar_processor],
    temperature=0.1
)

🌐 FastAPI Integration

from fastapi import FastAPI
from pydantic import BaseModel
import json

app = FastAPI()

class ToolCallRequest(BaseModel):
    query: str
    tools: list

@app.post("/tool-call")
async def generate_tool_call(request: ToolCallRequest):
    prompt = f"""Available tools:
{json.dumps(request.tools, indent=2)}

User query: {request.query}

Generate the appropriate tool call in JSON format.
"""
    
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.1)
    
    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
    json_str = result.split("Generate the appropriate tool call in JSON format.")[-1].strip()
    tool_call = json.loads(json_str)
    
    return {"tool_call": tool_call}

⚙️ Advanced: Streaming Responses

from transformers import TextIteratorStreamer
from threading import Thread

streamer = TextIteratorStreamer(tokenizer, skip_special_tokens=True)

generation_kwargs = dict(
    inputs=inputs,
    streamer=streamer,
    max_new_tokens=256,
    temperature=0.1
)

thread = Thread(target=model.generate, kwargs=generation_kwargs)
thread.start()

for new_text in streamer:
    print(new_text, end="", flush=True)

thread.join()

Examples

🎯 Real-World Use Cases

1️⃣ Mathematical Operations

📝 Query:

Calculate the dot product of 
vectors [2, 3, 1] and [4, -1, 2]

🤖 Output:

[
  {
    "name": "calculate_dot_product",
    "arguments": {
      "vector_a": [2, 3, 1],
      "vector_b": [4, -1, 2]
    }
  }
]

2️⃣ Data Processing

📝 Query:

Filter the list [1, 5, 12, 8, 3, 15] 
to keep only numbers greater than 7

🤖 Output:

[
  {
    "name": "filter_list",
    "arguments": {
      "data": [1, 5, 12, 8, 3, 15],
      "condition": "greater_than",
      "threshold": 7
    }
  }
]

3️⃣ Email Automation

📝 Query:

Send an email to john@example.com 
with subject 'Meeting Reminder' 
and body 'Don't forget our 
meeting at 3 PM'

🤖 Output:

[
  {
    "name": "send_email",
    "arguments": {
      "recipient": "john@example.com",
      "subject": "Meeting Reminder",
      "body": "Don't forget our meeting at 3 PM"
    }
  }
]

4️⃣ Database Queries

📝 Query:

Find all users in the database 
who registered after January 1, 2024 
and are from India

🤖 Output:

[
  {
    "name": "query_database",
    "arguments": {
      "table": "users",
      "filters": {
        "registration_date": {
          "operator": "greater_than",
          "value": "2024-01-01"
        },
        "country": {
          "operator": "equals",
          "value": "India"
        }
      }
    }
  }
]