Instructions to use ThingAI/Quark-270m-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ThingAI/Quark-270m-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="ThingAI/Quark-270m-Instruct", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("ThingAI/Quark-270m-Instruct", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ThingAI/Quark-270m-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ThingAI/Quark-270m-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ThingAI/Quark-270m-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ThingAI/Quark-270m-Instruct

SGLang

How to use ThingAI/Quark-270m-Instruct 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 "ThingAI/Quark-270m-Instruct" \
    --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": "ThingAI/Quark-270m-Instruct",
		"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 "ThingAI/Quark-270m-Instruct" \
        --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": "ThingAI/Quark-270m-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ThingAI/Quark-270m-Instruct with Docker Model Runner:
```
docker model run hf.co/ThingAI/Quark-270m-Instruct
```

Quark-270M-Instruct — Bilingual Chat Model

Quark-270M-Instruct is the instruction-tuned version of Quark-270M Base, fine-tuned for conversational use in Italian and English. Built entirely from scratch by ThingsAI.

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "ThingAI/Quark-270m-Instruct",
    trust_remote_code=True,
    torch_dtype=torch.bfloat16
).cuda()
model.lm_head.weight = model.embed_tokens.weight  # ensure weight tying

tokenizer = AutoTokenizer.from_pretrained("ThingAI/Quark-270m-Instruct")

prompt = "<|user|>\nCiao, come stai?\n<|end|>\n<|assistant|>\n"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
out = model.generate(**inputs, max_new_tokens=150, do_sample=True, temperature=0.7, top_k=40)
print(tokenizer.decode(out[0], skip_special_tokens=False))

Chat Format

<|user|>
{user message}
<|end|>
<|assistant|>
{model response}
<|end|>

Multi-turn:

<|user|>
Ciao!
<|end|>
<|assistant|>
Ciao! Come posso aiutarti?
<|end|>
<|user|>
Cos'è l'intelligenza artificiale?
<|end|>
<|assistant|>

Model Details


Base Model	Quark-270M Base
Parameters	252M (with weight tying)
Architecture	Decoder-only Transformer (GQA, SwiGLU, RMSNorm, RoPE)
Vocabulary	65,537 tokens
Context Length	2,048 tokens
Precision	BF16
Languages	Italian, English

Architecture


d_model	768
Layers	32
Query Heads	12
KV Heads	4
Head Dim	64
FFN Dim	2,048
Activation	SwiGLU

Training

Base Pretraining

~10B tokens on a bilingual mix (Italian 50%, English 43%, Code 7%) on NVIDIA B200. See Quark-270M Base for details.

SFT (Instruction Tuning)

Fine-tuned on a diverse mix of conversational and instructional data:

Dataset	Examples	Type
FreedomIntelligence/alpaca-gpt4-italian	~52,000	Italian instructions
HuggingFaceH4/no_robots	~9,500	English conversations
m-a-p/CodeFeedback-Filtered-Instruction	5,000	Code instructions
yogeshm/text_to_bash (×80)	~9,900	Terminal commands
Custom chitchat (×100)	~3,000	Identity, greetings, basic Q&A
Total	~80,000


Hardware	NVIDIA B200
Epochs	3
Learning Rate	2e-5 (cosine decay)
Batch Size	16 × 4 = 64 effective
Sequence Length	512

Inference Server

Quark-270M-Instruct powers Things Chat via a self-hosted FastAPI server with SSE streaming, conversation memory, web search, and content moderation.

Limitations

252M is small: Limited factual knowledge, prone to hallucination
Mathematics: Unreliable beyond basic arithmetic
Code: Generates plausible but often non-functional code
Context: 2,048 token window
No system prompt: The model was not trained with <|system|> tags

Good for

Self-hosted bilingual chatbot
Learning about LLM training from scratch
Terminal command assistance
Light conversational AI

Not suited for

Factual Q&A requiring accuracy
Complex reasoning or math
Production-grade code generation
Safety-critical applications

The Quark Family

Model	Parameters	Type
Quark-50M	51M	Base
Quark-135M	135M	Base
Quark-270M Base	252M	Base
Quark-270M-Instruct	252M	Chat

ThingAI
/

Quark-270m-Instruct