Instructions to use ilopezluna/something with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ilopezluna/something with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("ilopezluna/something")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Transformers

How to use ilopezluna/something with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ilopezluna/something")
model = AutoModelForCausalLM.from_pretrained("ilopezluna/something", 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 ilopezluna/something 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 ilopezluna/something:F16
# Run inference directly in the terminal:
llama cli -hf ilopezluna/something:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ilopezluna/something:F16
# Run inference directly in the terminal:
llama cli -hf ilopezluna/something:F16

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 ilopezluna/something:F16
# Run inference directly in the terminal:
./llama-cli -hf ilopezluna/something:F16

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 ilopezluna/something:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ilopezluna/something:F16

Use Docker

docker model run hf.co/ilopezluna/something:F16

LM Studio
Jan

vLLM

How to use ilopezluna/something with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ilopezluna/something:F16

SGLang

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

Ollama
How to use ilopezluna/something with Ollama:
```
ollama run hf.co/ilopezluna/something:F16
```

Unsloth Studio

How to use ilopezluna/something 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 ilopezluna/something 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 ilopezluna/something to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ilopezluna/something to start chatting

How to use ilopezluna/something with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "ilopezluna/something"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "ilopezluna/something"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ilopezluna/something with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "ilopezluna/something"

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default ilopezluna/something

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use ilopezluna/something with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "ilopezluna/something"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "ilopezluna/something" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

MLX LM

How to use ilopezluna/something with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "ilopezluna/something"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "ilopezluna/something"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "ilopezluna/something",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use ilopezluna/something with Docker Model Runner:
```
docker model run hf.co/ilopezluna/something:F16
```

Lemonade

How to use ilopezluna/something with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ilopezluna/something:F16

Run and chat with the model

lemonade run user.something-F16

List all available models

lemonade list

something / chat_template.jinja

ilopezluna

Add files using upload-large-folder tool

3061d2a verified about 1 year ago

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	{%- if messages[0]["role"] == "system" %}
	{%- set system_message = messages[0]["content"] %}
	{%- set loop_messages = messages[1:] %}
	{%- else %}
	{%- set loop_messages = messages %}
	{%- endif %}
	{%- if not tools is defined %}
	{%- set tools = none %}
	{%- endif %}
	{%- set user_messages = loop_messages \| selectattr("role", "equalto", "user") \| list %}

	{#- This block checks for alternating user/assistant messages, skipping tool calling messages #}
	{%- set ns = namespace() %}
	{%- set ns.index = 0 %}
	{%- for message in loop_messages %}
	{%- if not (message.role == "tool" or message.role == "tool_results" or (message.tool_calls is defined and message.tool_calls is not none)) %}
	{%- if (message["role"] == "user") != (ns.index % 2 == 0) %}
	{{- raise_exception("After the optional system message, conversation roles must alternate user/assistant/user/assistant/...") }}
	{%- endif %}
	{%- set ns.index = ns.index + 1 %}
	{%- endif %}
	{%- endfor %}

	{{- bos_token }}
	{%- for message in loop_messages %}
	{%- if message["role"] == "user" %}
	{%- if tools is not none and (message == user_messages[-1]) %}
	{{- "[AVAILABLE_TOOLS] [" }}
	{%- for tool in tools %}
	{%- set tool = tool.function %}
	{{- '{"type": "function", "function": {' }}
	{%- for key, val in tool.items() if key != "return" %}
	{%- if val is string %}
	{{- '"' + key + '": "' + val + '"' }}
	{%- else %}
	{{- '"' + key + '": ' + val\|tojson }}
	{%- endif %}
	{%- if not loop.last %}
	{{- ", " }}
	{%- endif %}
	{%- endfor %}
	{{- "}}" }}
	{%- if not loop.last %}
	{{- ", " }}
	{%- else %}
	{{- "]" }}
	{%- endif %}
	{%- endfor %}
	{{- "[/AVAILABLE_TOOLS]" }}
	{%- endif %}
	{%- if loop.last and system_message is defined %}
	{{- "[INST] " + system_message + "\n\n" + message["content"] + "[/INST]" }}
	{%- else %}
	{{- "[INST] " + message["content"] + "[/INST]" }}
	{%- endif %}
	{%- elif message.tool_calls is defined and message.tool_calls is not none %}
	{{- "[TOOL_CALLS] [" }}
	{%- for tool_call in message.tool_calls %}
	{%- set out = tool_call.function\|tojson %}
	{{- out[:-1] }}
	{%- if not tool_call.id is defined or tool_call.id\|length != 9 %}
	{{- raise_exception("Tool call IDs should be alphanumeric strings with length 9!") }}
	{%- endif %}
	{{- ', "id": "' + tool_call.id + '"}' }}
	{%- if not loop.last %}
	{{- ", " }}
	{%- else %}
	{{- "]" + eos_token }}
	{%- endif %}
	{%- endfor %}
	{%- elif message["role"] == "assistant" %}
	{{- " " + message["content"]\|trim + eos_token}}
	{%- elif message["role"] == "tool_results" or message["role"] == "tool" %}
	{%- if message.content is defined and message.content.content is defined %}
	{%- set content = message.content.content %}
	{%- else %}
	{%- set content = message.content %}
	{%- endif %}
	{{- '[TOOL_RESULTS] {"content": ' + content\|string + ", " }}
	{%- if not message.tool_call_id is defined or message.tool_call_id\|length != 9 %}
	{{- raise_exception("Tool call IDs should be alphanumeric strings with length 9!") }}
	{%- endif %}
	{{- '"call_id": "' + message.tool_call_id + '"}[/TOOL_RESULTS]' }}
	{%- else %}
	{{- raise_exception("Only user and assistant roles are supported, with the exception of an initial optional system message!") }}
	{%- endif %}
	{%- endfor %}