Instructions to use FlameF0X/Qwen2-0.2B-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FlameF0X/Qwen2-0.2B-it with Transformers:

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

pipe = pipeline("text-generation", model="FlameF0X/Qwen2-0.2B-it")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FlameF0X/Qwen2-0.2B-it")
model = AutoModelForCausalLM.from_pretrained("FlameF0X/Qwen2-0.2B-it")
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 FlameF0X/Qwen2-0.2B-it with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FlameF0X/Qwen2-0.2B-it"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FlameF0X/Qwen2-0.2B-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FlameF0X/Qwen2-0.2B-it

SGLang

How to use FlameF0X/Qwen2-0.2B-it 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 "FlameF0X/Qwen2-0.2B-it" \
    --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": "FlameF0X/Qwen2-0.2B-it",
		"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 "FlameF0X/Qwen2-0.2B-it" \
        --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": "FlameF0X/Qwen2-0.2B-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FlameF0X/Qwen2-0.2B-it with Docker Model Runner:
```
docker model run hf.co/FlameF0X/Qwen2-0.2B-it
```

Evaluation Results

Benchmark	Score
GSM8K (test)	2.00%
MMLU-Pro (test)	4.00%

Results obtained via local evaluation. Given the model size (0.2B parameters), low benchmark scores are expected.

Model Usage

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_path = "FlameF0X/Qwen2-0.2B-it"

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True
)

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Explain how a transformer model works in one sentence."}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=128,
    do_sample=True,
    temperature=0.7
)

generated_ids = [
    output_ids[len(input_ids):]
    for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(f"--- Assistant Response ---\n{response}")

Training Data

This model was instruction-tuned on a mixture of:

Salesforce/wikitext — General text
roneneldan/TinyStories — Short story generation
FlameF0X/arXiv-AI-ML — AI/ML research papers
Skylion007/openwebtext — Web text
flytech/python-codes-25k — Python code
bookcorpus/bookcorpus — Books
HuggingFaceH4/ultrachat_200k — Instruction following
openai/gsm8k — Math reasoning
microsoft/orca-math-word-problems-200k — Math word problems
laion/OIG — Open instruction generalist
microsoft/wiki_qa — Question answering

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Safetensors

Model size

0.2B params

Tensor type

F32

Model tree for FlameF0X/Qwen2-0.2B-it

Base model

FlameF0X/Qwen2-0.2B-pt

Finetuned

(1)

this model

Datasets used to train FlameF0X/Qwen2-0.2B-it

Evaluation results

Accuracy on GSM8K
test set Local Benchmark

2.000
Accuracy on MMLU-Pro
test set Local Benchmark

4.000