Instructions to use Pinkstackorg/Fijik1-1b-DPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Pinkstackorg/Fijik1-1b-DPO with Transformers:

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

pipe = pipeline("text-generation", model="Pinkstackorg/Fijik1-1b-DPO")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Pinkstackorg/Fijik1-1b-DPO")
model = AutoModelForCausalLM.from_pretrained("Pinkstackorg/Fijik1-1b-DPO", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Pinkstackorg/Fijik1-1b-DPO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Pinkstackorg/Fijik1-1b-DPO"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Pinkstackorg/Fijik1-1b-DPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Pinkstackorg/Fijik1-1b-DPO

SGLang

How to use Pinkstackorg/Fijik1-1b-DPO 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 "Pinkstackorg/Fijik1-1b-DPO" \
    --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": "Pinkstackorg/Fijik1-1b-DPO",
		"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 "Pinkstackorg/Fijik1-1b-DPO" \
        --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": "Pinkstackorg/Fijik1-1b-DPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use Pinkstackorg/Fijik1-1b-DPO with Docker Model Runner:
```
docker model run hf.co/Pinkstackorg/Fijik1-1b-DPO
```

Pinkstack commited on May 20, 2025

Commit

627fd4d

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@@ -24,7 +24,7 @@ library_name: transformers
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6710ba6af1279fe0dfe33afe/SE_vmS54Qm3Heu6sozIo3.png)
 # What is it
-    This is a 1.0 Fijik series with **1 billion** parameters, dense 56 layer transformer LLM based on Qwen2.5, specifically, it was merged using Mergekit to be twice as large as Qwen2.5 1.5B.
 After merging, we used a custom dataset mix meant for this model, to improve its performance even more.
 - **Step 1 for fine-tuning via unsloth:** SFT on an estimated 5 million tokens. (more or less)

 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6710ba6af1279fe0dfe33afe/SE_vmS54Qm3Heu6sozIo3.png)
 # What is it
+    This is a 1.0 Fijik series with **1 billion** parameters, dense 56 layer transformer LLM based on Qwen2.5, specifically, it was merged using Mergekit to be twice as large as Qwen2.5 0.5B.
 After merging, we used a custom dataset mix meant for this model, to improve its performance even more.
 - **Step 1 for fine-tuning via unsloth:** SFT on an estimated 5 million tokens. (more or less)