- Libraries
- MLX
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) # pip install -U transformers accelerate
# 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=256)
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
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -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
- Llama new
- 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 Desktop
- 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
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?" } ] }'