Instructions to use bartowski/reader-lm-1.5b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bartowski/reader-lm-1.5b-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="bartowski/reader-lm-1.5b-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("bartowski/reader-lm-1.5b-GGUF", dtype="auto")

llama-cpp-python

How to use bartowski/reader-lm-1.5b-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="bartowski/reader-lm-1.5b-GGUF",
	filename="reader-lm-1.5b-IQ3_M.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 bartowski/reader-lm-1.5b-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf bartowski/reader-lm-1.5b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf bartowski/reader-lm-1.5b-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf bartowski/reader-lm-1.5b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf bartowski/reader-lm-1.5b-GGUF: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 bartowski/reader-lm-1.5b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf bartowski/reader-lm-1.5b-GGUF: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 bartowski/reader-lm-1.5b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bartowski/reader-lm-1.5b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/bartowski/reader-lm-1.5b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use bartowski/reader-lm-1.5b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bartowski/reader-lm-1.5b-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bartowski/reader-lm-1.5b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bartowski/reader-lm-1.5b-GGUF:Q4_K_M

SGLang

How to use bartowski/reader-lm-1.5b-GGUF 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 "bartowski/reader-lm-1.5b-GGUF" \
    --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": "bartowski/reader-lm-1.5b-GGUF",
		"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 "bartowski/reader-lm-1.5b-GGUF" \
        --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": "bartowski/reader-lm-1.5b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use bartowski/reader-lm-1.5b-GGUF with Ollama:
```
ollama run hf.co/bartowski/reader-lm-1.5b-GGUF:Q4_K_M
```

Unsloth Studio new

How to use bartowski/reader-lm-1.5b-GGUF 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 bartowski/reader-lm-1.5b-GGUF 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 bartowski/reader-lm-1.5b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for bartowski/reader-lm-1.5b-GGUF to start chatting

Docker Model Runner
How to use bartowski/reader-lm-1.5b-GGUF with Docker Model Runner:
```
docker model run hf.co/bartowski/reader-lm-1.5b-GGUF:Q4_K_M
```

Lemonade

How to use bartowski/reader-lm-1.5b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bartowski/reader-lm-1.5b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.reader-lm-1.5b-GGUF-Q4_K_M

List all available models

lemonade list

vllm: ....does not appear to have a file named config.json

by paolovic - opened Sep 15, 2024

Discussion

paolovic

Sep 15, 2024

Hi,
since vLLM still requires GGUF models to have a config.json, is there the possibility to create one?

Thank you in advance
Best regards

bartowski

Owner Sep 16, 2024

@paolovic can you try the one I just added, and if it doesn't work can you give me your command so I can test it myself too?

paolovic

Sep 18, 2024

Hi @bartowski ,
thanks for your quick response!

With the config.json I get the following error:

File "/usr/lib64/python3.11/concurrent/futures/_base.py", line 449, in result
return self.__get_result()
       ^^^^^^^^^^^^^^^^^^^
File "/usr/lib64/python3.11/concurrent/futures/_base.py", line 401, in __get_result
raise self._exception
       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/runtime_resources/pip/abc/venv/lib64/python3.11/site-packages/ray/serve/_private/replica.py", line 631, in initialize_and_get_metadata
raise RuntimeError(traceback.format_exc()) from None
RuntimeError: Traceback (most recent call last):
File "/runtime_resources/pip/abc/venv/lib64/python3.11/site-packages/ray/serve/_private/replica.py", line 609, in initialize_and_get_metadata
await self._user_callable_wrapper.initialize_callable()
File "/runtime_resources/pip/abc/venv/lib64/python3.11/site-packages/ray/serve/_private/replica.py", line 901, in initialize_callable
await self._call_func_or_gen(
File "/runtime_resources/pip/abc/venv/lib64/python3.11/site-packages/ray/serve/_private/replica.py", line 867, in _call_func_or_gen
result = callable(*args, **kwargs)
         ^^^^^^^^^^^^^^^^^^^^^^^^^
File "/runtime_resources/pip/abc/venv/lib64/python3.11/site-packages/ray/serve/api.py", line 219, in __init__
cls.__init__(self, *args, **kwargs)
File "/u01/app/mlo/projects/llm-apis/ray_vllm_inference/vllm_serve.py", line 105, in __init__
self.engine = AsyncLLMEngine.from_engine_args(args)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/runtime_resources/pip/abc/venv/lib64/python3.11/site-packages/vllm/engine/async_llm_engine.py", line 726, in from_engine_args
engine_config = engine_args.create_engine_config()
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/runtime_resources/pip/abc/venv/lib64/python3.11/site-packages/vllm/engine/arg_utils.py", line 792, in create_engine_config
model_config = ModelConfig(
               ^^^^^^^^^^^^
File "/runtime_resources/pip/abc/venv/lib64/python3.11/site-packages/vllm/config.py", line 230, in __init__
self.multimodal_config = self._init_multimodal_config(
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/runtime_resources/pip/abc/venv/lib64/python3.11/site-packages/vllm/config.py", line 245, in _init_multimodal_config
if any(
      ^
TypeError: 'NoneType' object is not iterable

But, I'll try to link the model and tokenizer to vllm like they explained here
https://github.com/vllm-project/vllm/issues/8401

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