Instructions to use Volko76/Fablia-Qwen3-1.7B-Format1-WithName with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Volko76/Fablia-Qwen3-1.7B-Format1-WithName with Transformers:

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

pipe = pipeline("text-generation", model="Volko76/Fablia-Qwen3-1.7B-Format1-WithName")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Volko76/Fablia-Qwen3-1.7B-Format1-WithName")
model = AutoModelForCausalLM.from_pretrained("Volko76/Fablia-Qwen3-1.7B-Format1-WithName")
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 Volko76/Fablia-Qwen3-1.7B-Format1-WithName with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Volko76/Fablia-Qwen3-1.7B-Format1-WithName"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Volko76/Fablia-Qwen3-1.7B-Format1-WithName",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Volko76/Fablia-Qwen3-1.7B-Format1-WithName

SGLang

How to use Volko76/Fablia-Qwen3-1.7B-Format1-WithName 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 "Volko76/Fablia-Qwen3-1.7B-Format1-WithName" \
    --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": "Volko76/Fablia-Qwen3-1.7B-Format1-WithName",
		"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 "Volko76/Fablia-Qwen3-1.7B-Format1-WithName" \
        --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": "Volko76/Fablia-Qwen3-1.7B-Format1-WithName",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Volko76/Fablia-Qwen3-1.7B-Format1-WithName with Docker Model Runner:
```
docker model run hf.co/Volko76/Fablia-Qwen3-1.7B-Format1-WithName
```

Fablia-Qwen3-Format2 Merged Model

This repository contains the merged version of the Fablia-Qwen3-Format2 model, where the LoRA adapter has been merged with the base model.

Model Details

Base Model: Qwen/Qwen3-1.7B
LoRA Adapter: Volko76/Fablia-Qwen3-1.7B-LoRa-Format1-WithName
Merged Model: Full precision merged model ready for inference

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

# Load the merged model
tokenizer = AutoTokenizer.from_pretrained("Volko76/Fablia-Qwen3-1.7B-Format1-WithName", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    "Volko76/Fablia-Qwen3-1.7B-Format1-WithName",
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True
)

# Generate text
prompt = "P: Bonjour cher ami, que fais-tu ?\nL:"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)