Instructions to use abenzerps/K2-Horizon-3.7B-MLX-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use abenzerps/K2-Horizon-3.7B-MLX-8bit 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("abenzerps/K2-Horizon-3.7B-MLX-8bit")

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 abenzerps/K2-Horizon-3.7B-MLX-8bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "abenzerps/K2-Horizon-3.7B-MLX-8bit"

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": "abenzerps/K2-Horizon-3.7B-MLX-8bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use abenzerps/K2-Horizon-3.7B-MLX-8bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "abenzerps/K2-Horizon-3.7B-MLX-8bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "abenzerps/K2-Horizon-3.7B-MLX-8bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "abenzerps/K2-Horizon-3.7B-MLX-8bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use abenzerps/K2-Horizon-3.7B-MLX-8bit 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 "abenzerps/K2-Horizon-3.7B-MLX-8bit"

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 abenzerps/K2-Horizon-3.7B-MLX-8bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use abenzerps/K2-Horizon-3.7B-MLX-8bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "abenzerps/K2-Horizon-3.7B-MLX-8bit"

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 "abenzerps/K2-Horizon-3.7B-MLX-8bit" \
  --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"

K2-Horizon-3.7B-MLX-8bit / config.json

abenzerps

K2-Horizon-3.7B MLX 8-bit release

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	{
	"architectures": [
	"K2HorizonForCausalLM"
	],
	"attention_bias": false,
	"attention_dropout": 0.0,
	"attention_gate_func": null,
	"auto_map": {
	"AutoConfig": "configuration_k2_horizon.K2HorizonConfig",
	"AutoModel": "modeling_k2_horizon.K2HorizonModel",
	"AutoModelForCausalLM": "modeling_k2_horizon.K2HorizonForCausalLM"
	},
	"bos_token_id": 0,
	"decoder_sparse_step": 1,
	"dtype": "bfloat16",
	"eos_token_id": [
	1,
	250019
	],
	"head_dim": 128,
	"hidden_act": "silu",
	"hidden_size": 2560,
	"initializer_range": 0.02,
	"intermediate_size": 10240,
	"layernorm_num_groups": 2,
	"max_position_embeddings": 524288,
	"mlp_only_layers": [
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	],
	"model_type": "k2_horizon",
	"moe_gate_bias": false,
	"moe_intermediate_size": 0,
	"mova_num_experts": 0,
	"mova_num_experts_per_tok": 0,
	"norm_topk_prob": true,
	"num_attention_heads": 32,
	"num_experts": 0,
	"num_experts_per_tok": 0,
	"num_hidden_layers": 36,
	"num_key_value_heads": 8,
	"num_shared_experts": 0,
	"output_router_logits": false,
	"pad_token_id": null,
	"quantization": {
	"group_size": 64,
	"bits": 8,
	"mode": "affine"
	},
	"quantization_config": {
	"group_size": 64,
	"bits": 8,
	"mode": "affine"
	},
	"query_key_norm": false,
	"rms_norm_eps": 1e-06,
	"rope_head_dim": 128,
	"rope_scaling": null,
	"rope_theta": 10000000.0,
	"router_aux_loss_coef": 0.001,
	"router_scaling_factor": 1.0,
	"router_score_func": "sigmoid",
	"sliding_window": null,
	"tie_word_embeddings": false,
	"transformers_version": "5.13.0",
	"use_cache": true,
	"use_sliding_window": false,
	"vocab_size": 250624,
	"model_file": "k2_horizon_mlx.py"
	}