Text Generation
MLX
Safetensors
spark2_5
spark-x2.5
long-context
1m-context
conversational
custom_code
4-bit precision
Instructions to use abenzerps/Spark-X2.5-4B-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use abenzerps/Spark-X2.5-4B-MLX-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("abenzerps/Spark-X2.5-4B-MLX-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
- Pi
How to use abenzerps/Spark-X2.5-4B-MLX-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 "abenzerps/Spark-X2.5-4B-MLX-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": "abenzerps/Spark-X2.5-4B-MLX-4bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use abenzerps/Spark-X2.5-4B-MLX-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 "abenzerps/Spark-X2.5-4B-MLX-4bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "abenzerps/Spark-X2.5-4B-MLX-4bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "abenzerps/Spark-X2.5-4B-MLX-4bit", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use abenzerps/Spark-X2.5-4B-MLX-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 "abenzerps/Spark-X2.5-4B-MLX-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 abenzerps/Spark-X2.5-4B-MLX-4bit
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use abenzerps/Spark-X2.5-4B-MLX-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 "abenzerps/Spark-X2.5-4B-MLX-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 "abenzerps/Spark-X2.5-4B-MLX-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"
| # coding=utf-8 | |
| # Copyright 2026 The XHToken team and the HuggingFace Inc. team. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| from transformers import PretrainedConfig | |
| class Spark2_5Config(PretrainedConfig): | |
| model_type = "spark2_5" | |
| keys_to_ignore_at_inference = ["past_key_values"] | |
| base_model_tp_plan = { | |
| "layers.*.self_attn.q_k_v_proj": "colwise", | |
| "layers.*.self_attn.g_proj": "colwise", | |
| "layers.*.self_attn.out_proj": "rowwise", | |
| "layers.*.mlp.gate_proj": "colwise", | |
| "layers.*.mlp.up_proj": "colwise", | |
| "layers.*.mlp.down_proj": "rowwise", | |
| } | |
| base_model_pp_plan = { | |
| "embedding": (["input_ids"], ["inputs_embeds"]), | |
| "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), | |
| "norm": (["hidden_states"], ["hidden_states"]), | |
| } | |
| def __init__( | |
| self, | |
| vocab_size=32000, | |
| hidden_size=4096, | |
| intermediate_size=11008, | |
| num_hidden_layers=32, | |
| num_attention_heads=32, | |
| num_key_value_heads=None, | |
| hidden_act="gelu", | |
| max_position_embeddings=2048, | |
| initializer_range=0.02, | |
| rms_norm_eps=1e-6, | |
| use_cache=True, | |
| pad_token_id=None, | |
| bos_token_id=1, | |
| eos_token_id=2, | |
| tie_word_embeddings=False, | |
| rope_parameters=None, | |
| attention_bias=False, | |
| attention_dropout=0.0, | |
| mlp_bias=False, | |
| head_dim=None, | |
| headwise_attn_output_gate=False, | |
| gate_attn_act_mode="sigmoid", | |
| sliding_window=None, | |
| layer_types=None, | |
| **kwargs, | |
| ): | |
| self.vocab_size = vocab_size | |
| self.max_position_embeddings = max_position_embeddings | |
| self.hidden_size = hidden_size | |
| self.intermediate_size = intermediate_size | |
| self.num_hidden_layers = num_hidden_layers | |
| self.num_attention_heads = num_attention_heads | |
| if num_key_value_heads is None: | |
| num_key_value_heads = num_attention_heads | |
| if num_attention_heads % num_key_value_heads != 0: | |
| raise ValueError( | |
| f"num_attention_heads ({num_attention_heads}) must be divisible by num_key_value_heads ({num_key_value_heads})" | |
| ) | |
| self.num_key_value_heads = num_key_value_heads | |
| self.hidden_act = hidden_act | |
| self.initializer_range = initializer_range | |
| self.rms_norm_eps = rms_norm_eps | |
| self.use_cache = use_cache | |
| self.attention_bias = attention_bias | |
| self.attention_dropout = attention_dropout | |
| self.mlp_bias = mlp_bias | |
| self.head_dim = head_dim if head_dim is not None else self.hidden_size // self.num_attention_heads | |
| self.headwise_attn_output_gate = headwise_attn_output_gate | |
| self.gate_attn_act_mode = gate_attn_act_mode | |
| self.sliding_window = sliding_window | |
| self.rope_parameters = rope_parameters | |
| if layer_types is None: | |
| layer_types = ["full_attention"] * num_hidden_layers | |
| if len(layer_types) != num_hidden_layers: | |
| raise ValueError( | |
| f"layer_types length ({len(layer_types)}) must match num_hidden_layers ({num_hidden_layers})" | |
| ) | |
| self.layer_types = layer_types | |
| super().__init__( | |
| pad_token_id=pad_token_id, | |
| bos_token_id=bos_token_id, | |
| eos_token_id=eos_token_id, | |
| tie_word_embeddings=tie_word_embeddings, | |
| **kwargs, | |
| ) | |
| def get_rope_theta(self, layer_type): | |
| params = self.rope_parameters.get(layer_type, {}) | |
| return params.get("rope_theta", 10000) | |
| def get_partial_rotary_factor(self, layer_type): | |
| params = self.rope_parameters.get(layer_type, {}) | |
| return params.get("partial_rotary_factor", 1.0) | |
| __all__ = ["Spark2_5Config"] | |