Instructions to use AlejandroOlmedo/OpenThinker-7B-8bit-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AlejandroOlmedo/OpenThinker-7B-8bit-mlx with Transformers:

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

pipe = pipeline("text-generation", model="AlejandroOlmedo/OpenThinker-7B-8bit-mlx")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AlejandroOlmedo/OpenThinker-7B-8bit-mlx")
model = AutoModelForCausalLM.from_pretrained("AlejandroOlmedo/OpenThinker-7B-8bit-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 AlejandroOlmedo/OpenThinker-7B-8bit-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("AlejandroOlmedo/OpenThinker-7B-8bit-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 AlejandroOlmedo/OpenThinker-7B-8bit-mlx with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AlejandroOlmedo/OpenThinker-7B-8bit-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": "AlejandroOlmedo/OpenThinker-7B-8bit-mlx",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AlejandroOlmedo/OpenThinker-7B-8bit-mlx

SGLang

How to use AlejandroOlmedo/OpenThinker-7B-8bit-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 "AlejandroOlmedo/OpenThinker-7B-8bit-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": "AlejandroOlmedo/OpenThinker-7B-8bit-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 "AlejandroOlmedo/OpenThinker-7B-8bit-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": "AlejandroOlmedo/OpenThinker-7B-8bit-mlx",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

How to use AlejandroOlmedo/OpenThinker-7B-8bit-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 "AlejandroOlmedo/OpenThinker-7B-8bit-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": "AlejandroOlmedo/OpenThinker-7B-8bit-mlx"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use AlejandroOlmedo/OpenThinker-7B-8bit-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 "AlejandroOlmedo/OpenThinker-7B-8bit-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 AlejandroOlmedo/OpenThinker-7B-8bit-mlx

Run Hermes

hermes

MLX LM

How to use AlejandroOlmedo/OpenThinker-7B-8bit-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 "AlejandroOlmedo/OpenThinker-7B-8bit-mlx"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "AlejandroOlmedo/OpenThinker-7B-8bit-mlx"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "AlejandroOlmedo/OpenThinker-7B-8bit-mlx",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use AlejandroOlmedo/OpenThinker-7B-8bit-mlx with Docker Model Runner:
```
docker model run hf.co/AlejandroOlmedo/OpenThinker-7B-8bit-mlx
```

About:

A fully open-source family of reasoning models built using a dataset derived by distilling DeepSeek-R1.

This model is a fine-tuned version of Qwen/Qwen2.5-7B-Instruct on the OpenThoughts-114k dataset dataset. This model improves upon the Bespoke-Stratos-7B model, which used 17k examples (Bespoke-Stratos-17k dataset).

Special thanks to the folks at Open Thoughts for fine-tuning this version of Qwen/Qwen2.5-7B-Instruct. More information about it can be found here:

https://huggingface.co/open-thoughts/OpenThinker-7B (Base Model)

https://github.com/open-thoughts/open-thoughts (Open Thoughts Git Repo)

I simply converted it to MLX format (using mlx-lm version 0.20.5.) with a quantization of 8-bit for better performance on Apple Silicon Macs.

Other Types:

Link	Type	Size	Notes
[MLX] (https://huggingface.co/AlejandroOlmedo/OpenThinker-7B-8bit-mlx)	8-bit	8.10 GB	Best Quality
[MLX] (https://huggingface.co/AlejandroOlmedo/OpenThinker-7B-4bit-mlx)	4-bit	4.30 GB	Good Quality

AlejandroOlmedo/OpenThinker-7B-8bit-mlx

The Model AlejandroOlmedo/OpenThinker-7B-8bit-mlx was converted to MLX format from open-thoughts/OpenThinker-7B using mlx-lm version 0.20.5.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("AlejandroOlmedo/OpenThinker-7B-8bit-mlx")

prompt="hello"

if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )

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

Downloads last month: 3

Safetensors

Model size

2B params

Tensor type

F16

U32

MLX

Hardware compatibility

8-bit

Model tree for AlejandroOlmedo/OpenThinker-7B-8bit-mlx

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Finetuned

open-thoughts/OpenThinker-7B

Quantized

(16)

this model

AlejandroOlmedo
/

OpenThinker-7B-8bit-mlx

About:

Other Types:

AlejandroOlmedo/OpenThinker-7B-8bit-mlx

Use with mlx

Model tree for AlejandroOlmedo/OpenThinker-7B-8bit-mlx

Dataset used to train AlejandroOlmedo/OpenThinker-7B-8bit-mlx