Instructions to use turtle170/TinyCode-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use turtle170/TinyCode-4B with Transformers:

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

pipe = pipeline("text-generation", model="turtle170/TinyCode-4B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("turtle170/TinyCode-4B", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("turtle170/TinyCode-4B", trust_remote_code=True)
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 turtle170/TinyCode-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "turtle170/TinyCode-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "turtle170/TinyCode-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/turtle170/TinyCode-4B

SGLang

How to use turtle170/TinyCode-4B 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 "turtle170/TinyCode-4B" \
    --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": "turtle170/TinyCode-4B",
		"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 "turtle170/TinyCode-4B" \
        --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": "turtle170/TinyCode-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use turtle170/TinyCode-4B with Docker Model Runner:
```
docker model run hf.co/turtle170/TinyCode-4B
```

turtle170 commited on about 23 hours ago

Commit

001b994

verified ·

1 Parent(s): 7b5f341

Model save

Browse files

Files changed (3) hide show

README.md +3 -3
model.safetensors +1 -1
training_args.bin +1 -1

README.md CHANGED Viewed

@@ -1,17 +1,17 @@
 ---
-base_model: microsoft/Phi-4-mini-instruct
 library_name: transformers
 model_name: TinyCode-4B
 tags:
 - generated_from_trainer
-- sft
 - trl
 licence: license
 ---
 # Model Card for TinyCode-4B
-This model is a fine-tuned version of [microsoft/Phi-4-mini-instruct](https://huggingface.co/microsoft/Phi-4-mini-instruct).
 It has been trained using [TRL](https://github.com/huggingface/trl).
 ## Quick start

 ---
+base_model: turtle170/TinyCode-4B
 library_name: transformers
 model_name: TinyCode-4B
 tags:
 - generated_from_trainer
 - trl
+- sft
 licence: license
 ---
 # Model Card for TinyCode-4B
+This model is a fine-tuned version of [turtle170/TinyCode-4B](https://huggingface.co/turtle170/TinyCode-4B).
 It has been trained using [TRL](https://github.com/huggingface/trl).
 ## Quick start

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