Instructions to use radm/prophet-qwen3-4b-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use radm/prophet-qwen3-4b-sft with Transformers:

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

pipe = pipeline("text-generation", model="radm/prophet-qwen3-4b-sft")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("radm/prophet-qwen3-4b-sft")
model = AutoModelForCausalLM.from_pretrained("radm/prophet-qwen3-4b-sft")
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 radm/prophet-qwen3-4b-sft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "radm/prophet-qwen3-4b-sft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "radm/prophet-qwen3-4b-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/radm/prophet-qwen3-4b-sft

SGLang

How to use radm/prophet-qwen3-4b-sft 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 "radm/prophet-qwen3-4b-sft" \
    --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": "radm/prophet-qwen3-4b-sft",
		"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 "radm/prophet-qwen3-4b-sft" \
        --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": "radm/prophet-qwen3-4b-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use radm/prophet-qwen3-4b-sft 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 radm/prophet-qwen3-4b-sft 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 radm/prophet-qwen3-4b-sft to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for radm/prophet-qwen3-4b-sft to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="radm/prophet-qwen3-4b-sft",
    max_seq_length=2048,
)

Docker Model Runner
How to use radm/prophet-qwen3-4b-sft with Docker Model Runner:
```
docker model run hf.co/radm/prophet-qwen3-4b-sft
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card for prophet-qwen3-4b-sft

Model Details

Model Description

This model is a fine-tuned version of Qwen/Qwen3-4B. Training was conducted with Supervised Fine-Tuning (SFT) using the Unsloth library on a custom reasoning and non-reasoning dataset.

The model focuses on philosophical and esoteric topics and is multilingual.

Developed by: radm
Finetuned from model: Qwen/Qwen3-4B
Model type: Causal LM based on the Llama3 architecture
Language(s): Multilingual
License: Apache 2.0 (inherited from base model)

Uses

This is reasoning model, but you can add \n/no_think to user prompts or system messages to switch the model's thinking mode from turn to turn.

Out-of-Scope Use

The model is not designed for generating harmful, unethical, biased, or factually incorrect content. Performance on tasks outside its training domain (philosophical/esoteric chat) may be suboptimal.

Bias, Risks, and Limitations

The model inherits biases from its base model (Qwen/Qwen3-4B) and the fine-tuning datasets. It may generate plausible-sounding but incorrect or nonsensical information, especially on complex topics. Its "understanding" is based on patterns in the data, not genuine comprehension or consciousness. Use the outputs with critical judgment.

Training Details

Training Data

The model was fine-tuned used the custom reasoning and non-reasoning dataset

Training Procedure

Training was performed using the Unsloth library integrated with trl's SFTTrainer.

Framework: Unsloth + SFTTrainer
Base Model: Qwen/Qwen3-4B
LoRA Configuration:
- r: 768
- lora_alpha: 768
- lora_dropout: 0.0
- bias: "none"
- target_modules: ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
- use_rslora: True
- use_dora: True
Precision: Auto (bfloat16 / float16)
Quantization (load): 4-bit
Optimizer: Paged AdamW 8-bit
Learning Rate: 2e-5
LR Scheduler: Cosine
Warmup Steps: 10
Batch Size (per device): 1
Gradient Accumulation Steps: 64 (Effective Batch Size: 64)
Max Sequence Length: 4096
Epochs: 1