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", 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 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
```

Qwen2-0.2B-it / README.md

FlameF0X

Update README.md

f5f3968 verified 7 months ago

1.45 kB

library_name: transformers
base_model:
  - FlameF0X/Qwen2-0.2B-pt
license: apache-2.0
datasets:
  - Salesforce/wikitext
  - roneneldan/TinyStories
  - FlameF0X/arXiv-AI-ML
  - Skylion007/openwebtext
  - flytech/python-codes-25k
  - bookcorpus/bookcorpus
  - HuggingFaceH4/ultrachat_200k
  - openai/gsm8k
  - microsoft/orca-math-word-problems-200k
  - laion/OIG
  - microsoft/wiki_qa

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}")