Instructions to use mlx-community/K2-Horizon-3.7B-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlx-community/K2-Horizon-3.7B-4bit 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("mlx-community/K2-Horizon-3.7B-4bit")

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 Settings
LM Studio

How to use mlx-community/K2-Horizon-3.7B-4bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mlx-community/K2-Horizon-3.7B-4bit"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "mlx-community/K2-Horizon-3.7B-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use mlx-community/K2-Horizon-3.7B-4bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "mlx-community/K2-Horizon-3.7B-4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "mlx-community/K2-Horizon-3.7B-4bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "mlx-community/K2-Horizon-3.7B-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use mlx-community/K2-Horizon-3.7B-4bit 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 "mlx-community/K2-Horizon-3.7B-4bit"

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 mlx-community/K2-Horizon-3.7B-4bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use mlx-community/K2-Horizon-3.7B-4bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mlx-community/K2-Horizon-3.7B-4bit"

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 "mlx-community/K2-Horizon-3.7B-4bit" \
  --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"

Jianshu001 commited on 8 days ago

Commit

3b453d0

verified ·

1 Parent(s): 80066c6

Update model card for the mlx-community namespace

Browse files

Files changed (1) hide show

README.md +5 -5

README.md CHANGED Viewed

@@ -55,7 +55,7 @@ Smoke-tested after conversion with released mlx-lm 0.31.3: `17 * 23` → `391` a
 WikiText-2 test perplexity (128 × 512 tokens, lower is better) and generation speed on an M4 Max 128GB, all measured the same way:
-| | [bf16](https://huggingface.co/Jianshu001/K2-Horizon-3.7B-bf16) | [8-bit](https://huggingface.co/Jianshu001/K2-Horizon-3.7B-8bit) | [4-bit](https://huggingface.co/Jianshu001/K2-Horizon-3.7B-4bit) |
 |---|---|---|---|
 | Bits/weight | 16 | 8.5 | 6.5 |
 | Disk | 10.1 GB | 5.4 GB | 4.1 GB |
@@ -63,19 +63,19 @@ WikiText-2 test perplexity (128 × 512 tokens, lower is better) and generation s
 | WikiText-2 perplexity | 17.552 | 17.544 (-0.05%) | 18.324 (+4.4%) |
 | Generation | 50.3 tok/s | 84.2 tok/s | 109.6 tok/s |
-Perplexity is a coarse signal. Test the versions on your own workload before picking one. Other K2-Horizon sizes: the [K2-Horizon Dense collection](https://huggingface.co/collections/Jianshu001/k2-horizon-dense-6abb69400f675cbe43b29f82).
 ## Usage
 ```bash
-mlx_lm.generate --model Jianshu001/K2-Horizon-3.7B-4bit --trust-remote-code --prompt "Explain mixture-of-experts in two sentences." --max-tokens 2048
 ```
 ```python
 from mlx_lm import load, generate
-# Newer mlx-lm versions need trust_remote_code=True; on mlx-lm 0.31.3 use load("Jianshu001/K2-Horizon-3.7B-4bit").
-model, tokenizer = load("Jianshu001/K2-Horizon-3.7B-4bit", trust_remote_code=True)
 messages = [{"role": "user", "content": "Explain mixture-of-experts in two sentences."}]
 prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
 print(generate(model, tokenizer, prompt, max_tokens=2048))

 WikiText-2 test perplexity (128 × 512 tokens, lower is better) and generation speed on an M4 Max 128GB, all measured the same way:
+| | [bf16](https://huggingface.co/mlx-community/K2-Horizon-3.7B-bf16) | [8-bit](https://huggingface.co/mlx-community/K2-Horizon-3.7B-8bit) | [4-bit](https://huggingface.co/mlx-community/K2-Horizon-3.7B-4bit) |
 |---|---|---|---|
 | Bits/weight | 16 | 8.5 | 6.5 |
 | Disk | 10.1 GB | 5.4 GB | 4.1 GB |
 | WikiText-2 perplexity | 17.552 | 17.544 (-0.05%) | 18.324 (+4.4%) |
 | Generation | 50.3 tok/s | 84.2 tok/s | 109.6 tok/s |
+Perplexity is a coarse signal. Test the versions on your own workload before picking one. Other K2-Horizon sizes: the [K2-Horizon collection](https://huggingface.co/collections/mlx-community/k2-horizon).
 ## Usage
 ```bash
+mlx_lm.generate --model mlx-community/K2-Horizon-3.7B-4bit --trust-remote-code --prompt "Explain mixture-of-experts in two sentences." --max-tokens 2048
 ```
 ```python
 from mlx_lm import load, generate
+# Newer mlx-lm versions need trust_remote_code=True; on mlx-lm 0.31.3 use load("mlx-community/K2-Horizon-3.7B-4bit").
+model, tokenizer = load("mlx-community/K2-Horizon-3.7B-4bit", trust_remote_code=True)
 messages = [{"role": "user", "content": "Explain mixture-of-experts in two sentences."}]
 prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
 print(generate(model, tokenizer, prompt, max_tokens=2048))