Instructions to use Ramikan-BR/tinyllama-coder-py-v11 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ramikan-BR/tinyllama-coder-py-v11 with Transformers:

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

pipe = pipeline("text-generation", model="Ramikan-BR/tinyllama-coder-py-v11")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Ramikan-BR/tinyllama-coder-py-v11")
model = AutoModelForCausalLM.from_pretrained("Ramikan-BR/tinyllama-coder-py-v11", 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

llama.cpp

How to use Ramikan-BR/tinyllama-coder-py-v11 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Ramikan-BR/tinyllama-coder-py-v11:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Ramikan-BR/tinyllama-coder-py-v11:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Ramikan-BR/tinyllama-coder-py-v11:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Ramikan-BR/tinyllama-coder-py-v11:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Ramikan-BR/tinyllama-coder-py-v11:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Ramikan-BR/tinyllama-coder-py-v11:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Ramikan-BR/tinyllama-coder-py-v11:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Ramikan-BR/tinyllama-coder-py-v11:Q4_K_M

Use Docker

docker model run hf.co/Ramikan-BR/tinyllama-coder-py-v11:Q4_K_M

LM Studio
Jan

vLLM

How to use Ramikan-BR/tinyllama-coder-py-v11 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ramikan-BR/tinyllama-coder-py-v11"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ramikan-BR/tinyllama-coder-py-v11",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Ramikan-BR/tinyllama-coder-py-v11:Q4_K_M

SGLang

How to use Ramikan-BR/tinyllama-coder-py-v11 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 "Ramikan-BR/tinyllama-coder-py-v11" \
    --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": "Ramikan-BR/tinyllama-coder-py-v11",
		"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 "Ramikan-BR/tinyllama-coder-py-v11" \
        --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": "Ramikan-BR/tinyllama-coder-py-v11",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Ramikan-BR/tinyllama-coder-py-v11 with Ollama:
```
ollama run hf.co/Ramikan-BR/tinyllama-coder-py-v11:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use Ramikan-BR/tinyllama-coder-py-v11 with Docker Model Runner:
```
docker model run hf.co/Ramikan-BR/tinyllama-coder-py-v11:Q4_K_M
```

Lemonade

How to use Ramikan-BR/tinyllama-coder-py-v11 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Ramikan-BR/tinyllama-coder-py-v11:Q4_K_M

Run and chat with the model

lemonade run user.tinyllama-coder-py-v11-Q4_K_M

List all available models

lemonade list

Atomic Chat

datasets: code.evol.instruct.wiz.oss_python.json

==((====))==  Unsloth - 2x faster free finetuning | Num GPUs = 1
   \\   /|    Num examples = 937 | Num Epochs = 2
O^O/ \_/ \    Batch size per device = 2 | Gradient Accumulation steps = 256
\        /    Total batch size = 512 | Total steps = 2
 "-____-"     Number of trainable parameters = 201,850,880
 [2/2 22:36, Epoch 1/2]
Step	Training Loss
1	0.707400
2	0.717800

Uploaded model

Developed by: Ramikan-BR
License: apache-2.0
Finetuned from model : unsloth/tinyllama-chat-bnb-4bit

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.

Downloads last month: 61

Safetensors

Model size

1B params

Tensor type

F16

Model tree for Ramikan-BR/tinyllama-coder-py-v11

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unsloth/tinyllama-chat-bnb-4bit

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