Instructions to use fs90/Qwen3-1.7B-alpaca-cleaned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fs90/Qwen3-1.7B-alpaca-cleaned with Transformers:

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

pipe = pipeline("text-generation", model="fs90/Qwen3-1.7B-alpaca-cleaned")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("fs90/Qwen3-1.7B-alpaca-cleaned")
model = AutoModelForCausalLM.from_pretrained("fs90/Qwen3-1.7B-alpaca-cleaned")
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

vLLM

How to use fs90/Qwen3-1.7B-alpaca-cleaned with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fs90/Qwen3-1.7B-alpaca-cleaned"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fs90/Qwen3-1.7B-alpaca-cleaned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/fs90/Qwen3-1.7B-alpaca-cleaned

SGLang

How to use fs90/Qwen3-1.7B-alpaca-cleaned 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 "fs90/Qwen3-1.7B-alpaca-cleaned" \
    --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": "fs90/Qwen3-1.7B-alpaca-cleaned",
		"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 "fs90/Qwen3-1.7B-alpaca-cleaned" \
        --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": "fs90/Qwen3-1.7B-alpaca-cleaned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use fs90/Qwen3-1.7B-alpaca-cleaned with Docker Model Runner:
```
docker model run hf.co/fs90/Qwen3-1.7B-alpaca-cleaned
```

Qwen3-1.7B-alpaca-cleaned - Merged Model

Full-precision (16-bit) merged model with LoRA adapters integrated.

Model Details

Base Model: unsloth/Qwen3-1.7B-unsloth-bnb-4bit
Format: merged_16bit
Dataset: yahma/alpaca-cleaned
Size: ~8-16GB
Usage: transformers

Related Models

LoRA Adapters: fs90/Qwen3-1.7B-alpaca-cleaned-lora - Smaller LoRA-only adapters
GGUF Quantized: fs90/Qwen3-1.7B-alpaca-cleaned-gguf - GGUF format for llama.cpp/Ollama

Training Details

LoRA Rank: 16
Training Time: 53.0 minutes
Training Loss: 1.3403
Max Seq Length: 4096
Training Mode: Full training

For complete training configuration, see the LoRA adapters repository/directory.

Usage

With Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "./outputs/Qwen3-1.7B-alpaca-cleaned/merged_16bit",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("./outputs/Qwen3-1.7B-alpaca-cleaned/merged_16bit")

messages = [{"role": "user", "content": "Your question here"}]
inputs = tokenizer.apply_chat_template(messages, tokenize=True, return_tensors="pt").to("cuda")
outputs = model.generate(inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0]))

License

Based on unsloth/Qwen3-1.7B-unsloth-bnb-4bit and trained on yahma/alpaca-cleaned. Please refer to the original model and dataset licenses.

Framework Versions

Unsloth: 2025.11.3
Transformers: 4.57.1
PyTorch: 2.9.0+cu128

Generated: 2025-11-22 00:48:46

Downloads last month: 1

Safetensors

Model size

2B params

Tensor type

F16

Model tree for fs90/Qwen3-1.7B-alpaca-cleaned

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