Instructions to use vsan/tiny-pickle-v3-coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vsan/tiny-pickle-v3-coder with Transformers:

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

pipe = pipeline("text-generation", model="vsan/tiny-pickle-v3-coder")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("vsan/tiny-pickle-v3-coder")
model = AutoModelForCausalLM.from_pretrained("vsan/tiny-pickle-v3-coder", 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 vsan/tiny-pickle-v3-coder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vsan/tiny-pickle-v3-coder"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vsan/tiny-pickle-v3-coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/vsan/tiny-pickle-v3-coder

SGLang

How to use vsan/tiny-pickle-v3-coder 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 "vsan/tiny-pickle-v3-coder" \
    --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": "vsan/tiny-pickle-v3-coder",
		"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 "vsan/tiny-pickle-v3-coder" \
        --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": "vsan/tiny-pickle-v3-coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use vsan/tiny-pickle-v3-coder with Docker Model Runner:
```
docker model run hf.co/vsan/tiny-pickle-v3-coder
```

tiny-pickle-v3-coder

File size: 832 Bytes

5db6877

---
base_model: Qwen/Qwen3-Coder-30B-A3B-Instruct
library_name: transformers
pipeline_tag: text-generation
license: apache-2.0
tags:
- qwen3-coder
- coding
- software-engineering
- safetensors
- moe
- tiny-pickle
---

# Tiny Pickle v3 Coder

Merged Safetensors release of Tiny Pickle v3 Coder.

## Model lineage

- Base model: `Qwen/Qwen3-Coder-30B-A3B-Instruct`
- LoRA adapter: `vsan/tiny-pickle-v3-coder-LoRA`
- Format: merged BF16 Safetensors

## Intended use

Code generation, debugging, code review, implementation planning, test
generation, and software-engineering assistance.

## Limitations

This model is experimental and has not yet been proven superior to its
base model on independent execution-based benchmarks. Generated code may
be incorrect, insecure, incomplete, or non-functional and must be reviewed
and tested.