Instructions to use TheBlueObserver/Qwen2.5-1.5B-Instruct-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TheBlueObserver/Qwen2.5-1.5B-Instruct-MLX with Transformers:

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

pipe = pipeline("text-generation", model="TheBlueObserver/Qwen2.5-1.5B-Instruct-MLX")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TheBlueObserver/Qwen2.5-1.5B-Instruct-MLX")
model = AutoModelForCausalLM.from_pretrained("TheBlueObserver/Qwen2.5-1.5B-Instruct-MLX")
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]:]))

MLX

How to use TheBlueObserver/Qwen2.5-1.5B-Instruct-MLX 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("TheBlueObserver/Qwen2.5-1.5B-Instruct-MLX")

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)

Notebooks
Google Colab
Kaggle
Local Apps
LM Studio

vLLM

How to use TheBlueObserver/Qwen2.5-1.5B-Instruct-MLX with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TheBlueObserver/Qwen2.5-1.5B-Instruct-MLX"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TheBlueObserver/Qwen2.5-1.5B-Instruct-MLX",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TheBlueObserver/Qwen2.5-1.5B-Instruct-MLX

SGLang

How to use TheBlueObserver/Qwen2.5-1.5B-Instruct-MLX 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 "TheBlueObserver/Qwen2.5-1.5B-Instruct-MLX" \
    --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": "TheBlueObserver/Qwen2.5-1.5B-Instruct-MLX",
		"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 "TheBlueObserver/Qwen2.5-1.5B-Instruct-MLX" \
        --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": "TheBlueObserver/Qwen2.5-1.5B-Instruct-MLX",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Pi new

How to use TheBlueObserver/Qwen2.5-1.5B-Instruct-MLX with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "TheBlueObserver/Qwen2.5-1.5B-Instruct-MLX"

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": "TheBlueObserver/Qwen2.5-1.5B-Instruct-MLX"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use TheBlueObserver/Qwen2.5-1.5B-Instruct-MLX 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 "TheBlueObserver/Qwen2.5-1.5B-Instruct-MLX"

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 TheBlueObserver/Qwen2.5-1.5B-Instruct-MLX

Run Hermes

hermes

MLX LM

How to use TheBlueObserver/Qwen2.5-1.5B-Instruct-MLX with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "TheBlueObserver/Qwen2.5-1.5B-Instruct-MLX"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "TheBlueObserver/Qwen2.5-1.5B-Instruct-MLX"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "TheBlueObserver/Qwen2.5-1.5B-Instruct-MLX",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use TheBlueObserver/Qwen2.5-1.5B-Instruct-MLX with Docker Model Runner:
```
docker model run hf.co/TheBlueObserver/Qwen2.5-1.5B-Instruct-MLX
```

TheBlueObserver commited on Dec 14, 2024

Commit

4e7d9bb

verified ·

1 Parent(s): 366b4a3

Upload folder using huggingface_hub (#12)

Browse files

- 84e3aa78df2288a7aba9c88e5fd5e25465b49c102d98fd3a672b14a819286857 (baad0d22e3f78262516895488fd707e626676244)

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README.md +5 -5

README.md CHANGED Viewed

@@ -1,5 +1,5 @@
 ---
-base_model: TheBlueObserver/Qwen2.5-1.5B-Instruct
 language:
 - en
 library_name: transformers
@@ -12,10 +12,10 @@ tags:
 - mlx
 ---
-# TheBlueObserver/Qwen2.5-1.5B-Instruct-MLX
-The Model [TheBlueObserver/Qwen2.5-1.5B-Instruct-MLX](https://huggingface.co/TheBlueObserver/Qwen2.5-1.5B-Instruct-MLX) was
-converted to MLX format from [TheBlueObserver/Qwen2.5-1.5B-Instruct](https://huggingface.co/TheBlueObserver/Qwen2.5-1.5B-Instruct)
 using mlx-lm version **0.20.2**.
 ## Use with mlx
@@ -27,7 +27,7 @@ pip install mlx-lm
 ```python
 from mlx_lm import load, generate
-model, tokenizer = load("TheBlueObserver/Qwen2.5-1.5B-Instruct-MLX")
 prompt="hello"

 ---
+base_model: theblueobserver/Qwen2.5-1.5B-Instruct
 language:
 - en
 library_name: transformers
 - mlx
 ---
+# theblueobserver/Qwen2.5-1.5B-Instruct-MLX
+The Model [theblueobserver/Qwen2.5-1.5B-Instruct-MLX](https://huggingface.co/theblueobserver/Qwen2.5-1.5B-Instruct-MLX) was
+converted to MLX format from [theblueobserver/Qwen2.5-1.5B-Instruct](https://huggingface.co/theblueobserver/Qwen2.5-1.5B-Instruct)
 using mlx-lm version **0.20.2**.
 ## Use with mlx
 ```python
 from mlx_lm import load, generate
+model, tokenizer = load("theblueobserver/Qwen2.5-1.5B-Instruct-MLX")
 prompt="hello"