Text Generation
MLX
Safetensors
English
k2_horizon
mlx-lm
8-bit precision
k2-horizon
long-context
512k-context
dense
conversational
custom_code
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
- MLX
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
- Pi
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"
File size: 1,954 Bytes
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"architectures": [
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],
"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": [
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"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 2560,
"initializer_range": 0.02,
"intermediate_size": 10240,
"layernorm_num_groups": 2,
"max_position_embeddings": 524288,
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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"
} |