# FastChat — Build Your Own Open-Source ChatGPT Clone with LLM Chatbots

> Source: <https://dibi8.com/resources/llm-frameworks/fastchat-open-source-llm-chatbot-platform/>
> Published: 2026-07-17 00:00:00+00:00

# FastChat — Build Your Own Open-Source ChatGPT Clone with LLM Chatbots

Complete guide to LMSYS FastChat, the open-source platform for training, serving, and evaluating large language model chatbots. Build production AI assistants with Vicuna, Alpaca, and more.

- Updated 2026-07-17

## TL;DR [#](#tldr)

LMSYS FastChat is the most comprehensive open-source platform for building, serving, and evaluating LLM-powered chatbots in 2026. From fine-tuning models like Vicuna and Alpaca to deploying production-grade conversational AI, this guide covers every aspect of the FastChat ecosystem.

## What Is FastChat? [#](#what-is-fastchat)

FastChat is an open platform developed by the Large Model System Organization (LMSYS) for training, serving, and evaluating large language model chatbots. It provides tools for creating instruction-tuned models, running benchmarks, and deploying chat interfaces — all while maintaining full transparency and reproducibility.

### Key Features [#](#key-features)

**Model Training**: Train instruction-tuned models from base checkpoints using RLHF or direct preference optimization** Multiple Model Support**: Out-of-the-box support for Vicuna, Alpaca, LLaMA, and custom architectures** OpenAI-Compatible API**: Drop-in replacement for OpenAI’s API with your own models** Web Chat Interface**: Beautiful, responsive UI for testing and deploying chatbots** Evaluation Benchmarks**: Built-in evaluation on MT-Bench, AlpacaEval, and other standard benchmarks** Multi-GPU Serving**: Efficient deployment across multiple GPUs with tensor parallelism** Community Driven**: Backed by LMSYS Org’s research community with thousands of contributors

### Architecture Overview [#](#architecture-overview)

FastChat follows a modular architecture:

**FastChat Models**: Core model implementations supporting various architectures** FastChat Serve**: High-performance serving engine with OpenAI API compatibility** FastChat Train**: Training pipeline for instruction tuning and RLHF** FastChat Eval**: Evaluation framework for benchmarking model performance** FastChat Data**: Curated datasets for training and evaluation

#### The Model Worker Architecture [#](#the-model-worker-architecture)

FastChat uses a distributed worker architecture where each model runs as an independent worker process:

```
┌─────────────┐     ┌──────────────┐     ┌─────────────────┐
│  Controller  │────▶│  Model Worker│────▶│  Web UI / API   │
│  (port 21001)│     │  (port 21002)│     │  (port 7860)    │
└─────────────┘     └──────────────┘     └─────────────────┘
       │                    │
       │              ┌──────────────┐
       │              │  Model Worker│
       │              │  (port 21003)│
       │              └──────────────┘
       │
┌─────────────┐
│  Model Worker│
│  (port 21004)│
└─────────────┘
```

The controller manages load balancing, health checks, and routing between workers. Multiple workers can serve different models or handle requests for the same model in parallel.

## Installation Guide [#](#installation-guide)

### Quick Start with Docker [#](#quick-start-with-docker)

```
docker pull lmsysorg/fastchat:latest
docker run -p 8000:8000 lmsysorg/fastchat:latest
```

### Manual Installation [#](#manual-installation)

```
git clone https://github.com/lm-sys/FastChat.git
cd FastChat
pip install -e ".[model_worker,webui]"
```

Install specific model dependencies:

```
# For LLaMA-based models
pip install transformers accelerate

# For vLLM acceleration
pip install vllm

# For TensorRT-LLM
pip install tensorrt_llm
```

### Verify Installation [#](#verify-installation)

``` python
import fastchat
print(fastchat.__version__)

# Test model loading
from fastchat.model import load_model
model, tokenizer = load_model(
    "lmsys/vicuna-7b-v1.5",
    device="cuda"
)
print("Model loaded successfully!")
```

## Available Models [#](#available-models)

| Model | Parameters | Base Model | Best For |
|---|---|---|---|
| Vicuna-7B-v1.5 | 7B | LLaMA 2 | General conversation |
| Vicuna-13B-v1.5 | 13B | LLaMA 2 | Complex reasoning |
| Vicuna-33B-v1.5 | 33B | LLaMA 2 | Maximum capability |
| Alpaca-7B | 7B | LLaMA | Instruction following |
| Koala-13B | 13B | LLaMA | Academic tasks |
| ChatGLM2-6B | 6B | GLM | Chinese language |
| Baichuan2-7B | 7B | Baichuan | Chinese business |

### Loading Pre-trained Models [#](#loading-pre-trained-models)

``` python
from fastchat.model import load_model, get_conversation_template

model, tokenizer = load_model(
    "lmsys/vicuna-7b-v1.5",
    device="cuda",
    num_gpus=1,
    max_gpu_memory="22GiB"
)

# Create conversation template
conv = get_conversation_template("vicuna")
conv.append_message(conv.roles[0], "Hello, who are you?")
conv.append_message(conv.roles[1], None)

# Generate response
state = model.chat(
    conv,
    temperature=0.7,
    max_new_tokens=512
)

print(state.messages[-1][2])
```

## Building Your Own Chatbot [#](#building-your-own-chatbot)

### Step 1: Prepare Training Data [#](#step-1-prepare-training-data)

Create instruction-response pairs:

```
[
    {
        "instruction": "Explain quantum computing in simple terms.",
        "input": "",
        "output": "Quantum computing uses quantum bits (qubits) that can exist in multiple states simultaneously..."
    },
    {
        "instruction": "Write a Python function to calculate Fibonacci numbers.",
        "input": "",
        "output": "def fibonacci(n):\n    if n <= 1:\n        return n\n    return fibonacci(n-1) + fibonacci(n-2)"
    }
]
```

### Step 2: Fine-Tune with FastChat Train [#](#step-2-fine-tune-with-fastchat-train)

```
python -m fastchat.train.train \
    --model_name_or_path lmsys/vicuna-7b-v1.5 \
    --data_path ./training_data.json \
    --output_dir ./my-finetuned-model \
    --num_train_epochs 3 \
    --per_device_train_batch_size 4 \
    --gradient_accumulation_steps 8 \
    --learning_rate 2e-5 \
    --fp16 True \
    --save_steps 100 \
    --logging_steps 10 \
    --lr_scheduler_type cosine \
    --warmup_ratio 0.03 \
    --weight_decay 0.0 \
    --max_seq_length 2048
```

### Step 3: Evaluate Your Model [#](#step-3-evaluate-your-model)

```
python -m fastchat.eval.evaluate_benchmark \
    --model-path ./my-finetuned-model \
    --benchmark mt_bench \
    --num-questions 800
```

## Deploying to Production [#](#deploying-to-production)

### OpenAI-Compatible API Server [#](#openai-compatible-api-server)

``` python
from fastapi import FastAPI
from pydantic import BaseModel
from fastchat.serve.api_provider import OpenAIAPIClient

app = FastAPI()

class ChatRequest(BaseModel):
    model: str
    messages: list
    temperature: float = 0.7
    max_tokens: int = 2048

@app.post("/v1/chat/completions")
async def create_chat_completion(request: ChatRequest):
    client = OpenAIAPIClient(
        model_name=request.model,
        temperature=request.temperature
    )
    
    response = await client.chat_completion(
        messages=request.messages,
        max_tokens=request.max_tokens
    )
    
    return response
```

Start the server:

```
python -m fastchat.serve.openai_api_server \
    --model-path lmsys/vicuna-7b-v1.5 \
    --host 0.0.0.0 \
    --port 8000
```

Test with curl:

```
curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "vicuna-7b",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'
```

### Multi-GPU Deployment [#](#multi-gpu-deployment)

For models larger than a single GPU can hold:

```
python -m fastchat.serve.multi_model_worker \
    --model-path lmsys/vicuna-33b-v1.5 \
    --num-gpus 4 \
    --worker-address http://worker1:21001
```

Configure model parallelism:

``` python
from fastchat.serve.model_worker import ModelWorker

worker = ModelWorker(
    controller_address="http://controller:21001",
    worker_address="http://worker1:21002",
    model_names=["vicuna-33b"],
    model_path="lmsys/vicuna-33b-v1.5",
    num_gpus=4
)
```

### Kubernetes Deployment [#](#kubernetes-deployment)

```
apiVersion: apps/v1
kind: Deployment
metadata:
  name: fastchat-service
spec:
  replicas: 2
  selector:
    matchLabels:
      app: fastchat
  template:
    spec:
      containers:
      - name: fastchat
        image: lmsysorg/fastchat:v1.0
        command: ["python", "-m", "fastchat.serve.openai_api_server"]
        args:
          - "--model-path"
          - "lmsys/vicuna-13b-v1.5"
          - "--host"
          - "0.0.0.0"
          - "--port"
          - "8000"
        resources:
          limits:
            nvidia.com/gpu: 1
        ports:
        - containerPort: 8000
```

### Load Balancing with Multiple Workers [#](#load-balancing-with-multiple-workers)

Deploy multiple worker instances behind a load balancer:

```
# Worker 1
python -m fastchat.serve.model_worker \
    --controller-address http://controller:21001 \
    --worker-address http://worker1:21002 \
    --model-path lmsys/vicuna-7b-v1.5 \
    --limit-worker-concurrency 16

# Worker 2
python -m fastchat.serve.model_worker \
    --controller-address http://controller:21001 \
    --worker-address http://worker2:21003 \
    --model-path lmsys/vicuna-7b-v1.5 \
    --limit-worker-concurrency 16
```

## Web Chat Interface [#](#web-chat-interface)

### Launch the Demo UI [#](#launch-the-demo-ui)

```
python -m fastchat.serve.webui --host 0.0.0.0 --port 7860
```

Access at `http://localhost:7860`

to interact with your deployed model through a beautiful web interface.

### Customizing the UI [#](#customizing-the-ui)

Modify `fastchat/serve/gradio_web_server.py`

to customize:

- Brand colors and logos
- Available models list
- Temperature and parameter controls
- Conversation history management
- Export functionality

### Embedding in External Applications [#](#embedding-in-external-applications)

Embed the chat interface in your existing application:

```
<iframe 
    src="http://your-fastchat-server:7860/embed" 
    width="100%" 
    height="600px"
    frameborder="0">
</iframe>
```

## Advanced Topics [#](#advanced-topics)

### Reinforcement Learning from Human Feedback (RLHF) [#](#reinforcement-learning-from-human-feedback-rlhf)

Train models using human preferences:

```
python -m fastchat.train.rlhf.train \
    --model_name_or_path lmsys/vicuna-7b-v1.5 \
    --ref_model_path lmsys/vicuna-7b-v1.5 \
    --data_path ./human_feedback_data.json \
    --output_dir ./rlhf-finetuned-model \
    --num_train_epochs 1 \
    --learning_rate 1e-5
```

### Direct Preference Optimization (DPO) [#](#direct-preference-optimization-dpo)

Alternative to RLHF that directly optimizes policy from preference data:

```
python -m fastchat.train.dpo.train \
    --model_name_or_path lmsys/vicuna-7b-v1.5 \
    --data_path ./preference_data.json \
    --output_dir ./dpo-finetuned-model \
    --num_train_epochs 3 \
    --learning_rate 1e-5 \
    --per_device_train_batch_size 2 \
    --gradient_accumulation_steps 8
```

### Model Quantization for Edge Deployment [#](#model-quantization-for-edge-deployment)

Reduce model size for mobile or edge devices:

```
# Quantize to 4-bit
python -m fastchat.model.quantize quantize \
    --model-path lmsys/vicuna-7b-v1.5 \
    --output-path ./vicuna-7b-q4 \
    --bits 4

# Load quantized model
from fastchat.model import load_model
model, tokenizer = load_model("./vicuna-7b-q4", device="cpu")
```

### Evaluating with MT-Bench [#](#evaluating-with-mt-bench)

Run the official MT-Bench evaluation:

```
python -m fastchat.eval.evaluate_mtbench \
    --model-path ./my-finetuned-model \
    --judge-model lmsys/vicuna-13b-v1.5 \
    --output-file ./mtbench_results.json
```

### Streaming Responses [#](#streaming-responses)

Enable streaming for real-time token generation:

``` python
from fastchat.serve.stream_manager import StreamManager

stream_manager = StreamManager(
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=512,
    temperature=0.7
)

for token in stream_manager.stream(conv):
    print(token, end="", flush=True)
```

## Performance Comparison [#](#performance-comparison)

| Configuration | Tokens/sec | VRAM | Latency (p99) |
|---|---|---|---|
| Vicuna-7B + CPU | 15 tok/s | N/A | 2.5s |
| Vicuna-7B + RTX 3090 | 45 tok/s | 12 GB | 0.8s |
| Vicuna-13B + 2x A100 | 30 tok/s | 48 GB | 1.2s |
| Vicuna-33B + 4x A100 | 18 tok/s | 96 GB | 2.0s |
| Vicuna-7B + vLLM | 80 tok/s | 8 GB | 0.4s |

## Integration Examples [#](#integration-examples)

### ChatGPT Plugin Integration [#](#chatgpt-plugin-integration)

```
# Use FastChat as a backend for ChatGPT plugins
from fastchat.serve.api_provider import OpenAIAPIClient

client = OpenAIAPIClient(model_name="custom-vicuna")

response = client.chat_completion(
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is machine learning?"}
    ]
)
```

### LangChain Integration [#](#langchain-integration)

``` python
from langchain.llms import HuggingFacePipeline
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load model
tokenizer = AutoTokenizer.from_pretrained("lmsys/vicuna-7b-v1.5")
model = AutoModelForCausalLM.from_pretrained(
    "lmsys/vicuna-7b-v1.5",
    torch_dtype=torch.float16,
    device_map="auto"
)

# Create pipeline
pipeline = HuggingFacePipeline(
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=512,
    temperature=0.7
)

# Use with LangChain
from langchain.chains import ConversationChain
conversation = ConversationChain(llm=pipeline)
response = conversation.predict(input="Tell me about AI.")
```

### RAG (Retrieval-Augmented Generation) Pipeline [#](#rag-retrieval-augmented-generation-pipeline)

Combine FastChat with vector databases for knowledge-grounded responses:

``` python
from langchain.vectorstores import FAISS
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.chains import RetrievalQA

# Load vector store
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
vectorstore = FAISS.load_local("./knowledge_base", embeddings)

# Create retriever
retriever = vectorstore.as_retriever(search_kwargs={"k": 5})

# Build RAG chain
qa_chain = RetrievalQA.from_chain_type(
    llm=pipeline,
    chain_type="stuff",
    retriever=retriever
)

# Query with context
result = qa_chain.run("What are the key features of this product?")
```

## Production Checklist [#](#production-checklist)

- Set up monitoring with Ray Dashboard
- Configure autoscaling for variable workloads
- Implement retry logic for transient failures
- Use streaming responses for better UX
- Monitor GPU utilization and memory usage
- Set up alerts for cluster health
- Document resource requirements for each deployment
- Implement rate limiting for API endpoints
- Add input sanitization to prevent prompt injection
- Set up logging for audit trails

## FAQ [#](#faq)

### Q1: How does FastChat compare to Ollama? [#](#q1-how-does-fastchat-compare-to-ollama)

FastChat provides more flexibility for custom model training and evaluation, while Ollama focuses on simplicity. FastChat supports more model architectures and offers better control over serving configurations.

### Q2: Can I use FastChat with non-Llama models? [#](#q2-can-i-use-fastchat-with-non-llama-models)

Yes, FastChat supports LLaMA, Mistral, Falcon, BLOOM, GPT-J, and many other architectures. The model registry includes dozens of pre-configured templates.

### Q3: What hardware do I need for production deployment? [#](#q3-what-hardware-do-i-need-for-production-deployment)

Minimum: 1x GPU with 12GB VRAM for 7B models. Recommended: 2x A100 40GB for 13B+ models. For high-throughput serving, consider vLLM or TensorRT-LLM acceleration.

### Q4: How do I handle concurrent users? [#](#q4-how-do-i-handle-concurrent-users)

Use FastChat’s built-in load balancing with multiple model workers. Each worker handles a portion of requests, and the controller distributes traffic automatically.

### Q5: Is FastChat suitable for commercial use? [#](#q5-is-fastchat-suitable-for-commercial-use)

Yes, FastChat is MIT licensed. However, check the licenses of individual models you train or serve, as some base models may have additional restrictions.

### Q6: How do I prevent prompt injection attacks? [#](#q6-how-do-i-prevent-prompt-injection-attacks)

Implement input filtering, use system prompts to define behavior boundaries, and consider adding a separate moderation model to scan user inputs before passing them to the main model.

### Q7: Can I add custom tools/function calling to FastChat? [#](#q7-can-i-add-custom-toolsfunction-calling-to-fastchat)

Yes, FastChat supports function calling through custom conversation templates. You can define tool schemas and let the model decide which tools to call based on user intent.

## Sources [#](#sources)

[FastChat GitHub Repository](https://github.com/lm-sys/FastChat)[LMSYS Org Research](https://lmsys.org/)[Vicuna Model Card](https://lmsys.org/blog/2023-03-30-vicuna/)[MT-Bench Evaluation](https://huggingface.co/datasets/lmsys/mt_bench)[FastChat Documentation](https://github.com/lm-sys/FastChat/blob/main/docs/)

## Call to Action [#](#call-to-action)

Build your own AI assistant with FastChat’s open-source platform. [Get started](https://dibi8.com/auth/) with our deployment guides and model training tutorials.