Instructions to use ilyusha07/tinyllama-linux-command with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ilyusha07/tinyllama-linux-command with Transformers:

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

pipe = pipeline("text-generation", model="ilyusha07/tinyllama-linux-command")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ilyusha07/tinyllama-linux-command")
model = AutoModelForCausalLM.from_pretrained("ilyusha07/tinyllama-linux-command")
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]:]))

llama-cpp-python

How to use ilyusha07/tinyllama-linux-command with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ilyusha07/tinyllama-linux-command",
	filename="tinyllama-linux.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use ilyusha07/tinyllama-linux-command with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ilyusha07/tinyllama-linux-command
# Run inference directly in the terminal:
llama-cli -hf ilyusha07/tinyllama-linux-command

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ilyusha07/tinyllama-linux-command
# Run inference directly in the terminal:
llama-cli -hf ilyusha07/tinyllama-linux-command

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 ilyusha07/tinyllama-linux-command
# Run inference directly in the terminal:
./llama-cli -hf ilyusha07/tinyllama-linux-command

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 ilyusha07/tinyllama-linux-command
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ilyusha07/tinyllama-linux-command

Use Docker

docker model run hf.co/ilyusha07/tinyllama-linux-command

LM Studio
Jan

vLLM

How to use ilyusha07/tinyllama-linux-command with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ilyusha07/tinyllama-linux-command"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ilyusha07/tinyllama-linux-command",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ilyusha07/tinyllama-linux-command

SGLang

How to use ilyusha07/tinyllama-linux-command 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 "ilyusha07/tinyllama-linux-command" \
    --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": "ilyusha07/tinyllama-linux-command",
		"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 "ilyusha07/tinyllama-linux-command" \
        --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": "ilyusha07/tinyllama-linux-command",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use ilyusha07/tinyllama-linux-command with Ollama:
```
ollama run hf.co/ilyusha07/tinyllama-linux-command
```

Unsloth Studio new

How to use ilyusha07/tinyllama-linux-command with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ilyusha07/tinyllama-linux-command to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ilyusha07/tinyllama-linux-command to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ilyusha07/tinyllama-linux-command to start chatting

Docker Model Runner
How to use ilyusha07/tinyllama-linux-command with Docker Model Runner:
```
docker model run hf.co/ilyusha07/tinyllama-linux-command
```

Lemonade

How to use ilyusha07/tinyllama-linux-command with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ilyusha07/tinyllama-linux-command

Run and chat with the model

lemonade run user.tinyllama-linux-command-{{QUANT_TAG}}

List all available models

lemonade list

tinyllama-linux-command

File size: 1,398 Bytes

a3a778f

{
  "add_bos_token": true,
  "add_eos_token": false,
  "add_prefix_space": null,
  "added_tokens_decoder": {
    "0": {
      "content": "<unk>",
      "lstrip": false,
      "normalized": false,
      "rstrip": false,
      "single_word": false,
      "special": true
    },
    "1": {
      "content": "<s>",
      "lstrip": false,
      "normalized": false,
      "rstrip": false,
      "single_word": false,
      "special": true
    },
    "2": {
      "content": "</s>",
      "lstrip": false,
      "normalized": false,
      "rstrip": false,
      "single_word": false,
      "special": true
    }
  },
  "bos_token": "<s>",
  "chat_template": "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\n'  + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}",
  "clean_up_tokenization_spaces": false,
  "eos_token": "</s>",
  "extra_special_tokens": {},
  "legacy": false,
  "model_max_length": 2048,
  "pad_token": "</s>",
  "padding_side": "right",
  "sp_model_kwargs": {},
  "tokenizer_class": "LlamaTokenizer",
  "unk_token": "<unk>",
  "use_default_system_prompt": false
}