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

Libraries

How to use Waggerra/classifier with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Waggerra/classifier")
model = AutoModelForCausalLM.from_pretrained("Waggerra/classifier")
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

vLLM

How to use Waggerra/classifier with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Waggerra/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": "Waggerra/classifier",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Waggerra/classifier

SGLang

How to use Waggerra/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 "Waggerra/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": "Waggerra/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 "Waggerra/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": "Waggerra/classifier",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use Waggerra/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 Waggerra/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 Waggerra/classifier to start chatting

Using HuggingFace Spaces for Unsloth

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

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Waggerra/classifier",
    max_seq_length=2048,
)

Docker Model Runner
How to use Waggerra/classifier with Docker Model Runner:
```
docker model run hf.co/Waggerra/classifier
```

Waggerra commited on Jan 17, 2025

Commit

1b7e469

verified ·

1 Parent(s): b445666

Update config.json

Browse files

Files changed (1) hide show

config.json +13 -28

config.json CHANGED Viewed

@@ -1,29 +1,14 @@
 {
-    "model_config": {
-        "architectures": ["BloomForCausalLM"],
-        "model_type": "phi",
-        "name": "Waggerra/classifier",
-        "base_model": "microsoft/phi-3",
-        "fine_tuned": true
-    },
-    "inference_config": {
-        "temperature": 0.7,
-        "top_p": 0.9,
-        "max_length": 2048,
-        "do_sample": true
-    },
-    "training_config": {
-        "learning_rate": 2e-5,
-        "num_train_epochs": 3,
-        "per_device_train_batch_size": 4,
-        "gradient_accumulation_steps": 4
-    },
-    "metadata": {
-        "author": "Waggerra",
-        "model_name": "classifier",
-        "description": "Phi-3 3B model fine-tuned for classification tasks",
-        "tags": ["classification", "phi-3", "fine-tuned"],
-        "license": "mit",
-        "language": ["en"]
-    }
-}

 {
+    "architectures": [
+        "BloomForCausalLM"
+    ],
+    "vocab_size": 50257,
+    "hidden_size": 4096,
+    "tie_word_embeddings": true,
+    "n_layer": 30,
+    "hidden_dropout": 0.0,
+    "layer_norm_epsilon": 1e-05,
+    "n_head": 32,
+    "attention_dropout": 0.0,
+    "model_type": "bloom"
+}