Instructions to use razisayyed/intent-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use razisayyed/intent-classifier with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("razisayyed/intent-classifier")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Transformers

How to use razisayyed/intent-classifier with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="razisayyed/intent-classifier")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("razisayyed/intent-classifier")
model = AutoModelForCausalLM.from_pretrained("razisayyed/intent-classifier", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use razisayyed/intent-classifier with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf razisayyed/intent-classifier:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf razisayyed/intent-classifier:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf razisayyed/intent-classifier:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf razisayyed/intent-classifier:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf razisayyed/intent-classifier:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf razisayyed/intent-classifier:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf razisayyed/intent-classifier:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf razisayyed/intent-classifier:Q4_K_M

Use Docker

docker model run hf.co/razisayyed/intent-classifier:Q4_K_M

LM Studio
Jan

vLLM

How to use razisayyed/intent-classifier with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "razisayyed/intent-classifier"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "razisayyed/intent-classifier",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/razisayyed/intent-classifier:Q4_K_M

SGLang

How to use razisayyed/intent-classifier with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "razisayyed/intent-classifier" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "razisayyed/intent-classifier",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "razisayyed/intent-classifier" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "razisayyed/intent-classifier",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use razisayyed/intent-classifier with Ollama:
```
ollama run hf.co/razisayyed/intent-classifier:Q4_K_M
```

Unsloth Studio

How to use razisayyed/intent-classifier with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for razisayyed/intent-classifier to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for razisayyed/intent-classifier to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for razisayyed/intent-classifier to start chatting

MLX LM

How to use razisayyed/intent-classifier with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "razisayyed/intent-classifier"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "razisayyed/intent-classifier"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "razisayyed/intent-classifier",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use razisayyed/intent-classifier with Docker Model Runner:
```
docker model run hf.co/razisayyed/intent-classifier:Q4_K_M
```

Lemonade

How to use razisayyed/intent-classifier with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull razisayyed/intent-classifier:Q4_K_M

Run and chat with the model

lemonade run user.intent-classifier-Q4_K_M

List all available models

lemonade list

Atomic Chat

intent-classifier

File size: 1,158 Bytes

4c91b5a

{%- if messages | length == 0 -%}
    {{- raise_exception("The messages list cannot be empty.") -}}
{%- endif -%}

{%- if messages[0]["role"] == "system" -%}
    {{- "<|im_start|>system\n" -}}
    {{- messages[0]["content"] -}}
    {{- "<|im_end|>\n" -}}
    {%- set conversation_messages = messages[1:] -%}
{%- else -%}
    {{- "<|im_start|>system\n" -}}
    {{- "You are an intent classification assistant tasked with categorizing user questions into one of six predefined categories. You will receive a single question as input and must output only the corresponding label name without any additional text or explanations." -}}
    {{- "<|im_end|>\n" -}}
    {%- set conversation_messages = messages -%}
{%- endif -%}

{%- for message in conversation_messages -%}
    {%- if message["role"] not in ["user", "assistant"] -%}
        {{- raise_exception(
            "Unsupported message role: " + message["role"]
        ) -}}
    {%- endif -%}

    {{- "<|im_start|>" + message["role"] + "\n" -}}
    {{- message["content"] -}}
    {{- "<|im_end|>\n" -}}
{%- endfor -%}

{%- if add_generation_prompt -%}
    {{- "<|im_start|>assistant\n" -}}
{%- endif -%}