Text Generation
Safetensors
MLX
Hebrew
English
mamba
nemotron_h
mamba2
Mixture of Experts
hebrew
finance
legal
ssm
mlx-my-repo
conversational
custom_code
Instructions to use ssdataanalysis/Hebatron-mlx-fp16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use ssdataanalysis/Hebatron-mlx-fp16 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("ssdataanalysis/Hebatron-mlx-fp16") 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 ssdataanalysis/Hebatron-mlx-fp16 with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "ssdataanalysis/Hebatron-mlx-fp16"
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": "ssdataanalysis/Hebatron-mlx-fp16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use ssdataanalysis/Hebatron-mlx-fp16 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 "ssdataanalysis/Hebatron-mlx-fp16"
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 ssdataanalysis/Hebatron-mlx-fp16
Run Hermes
hermes
- MLX LM
How to use ssdataanalysis/Hebatron-mlx-fp16 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "ssdataanalysis/Hebatron-mlx-fp16"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "ssdataanalysis/Hebatron-mlx-fp16" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ssdataanalysis/Hebatron-mlx-fp16", "messages": [ {"role": "user", "content": "Hello"} ] }'
File size: 2,041 Bytes
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"architectures": [
"NemotronHForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"auto_map": {
"AutoConfig": "configuration_nemotron_h.NemotronHConfig",
"AutoModel": "modeling_nemotron_h.NemotronHForCausalLM",
"AutoModelForCausalLM": "modeling_nemotron_h.NemotronHForCausalLM"
},
"bos_token_id": 1,
"chunk_size": 128,
"conv_kernel": 4,
"dtype": "bfloat16",
"eos_token_id": [
2,
11
],
"expand": 2,
"head_dim": 128,
"hidden_dropout": 0.0,
"hidden_size": 2688,
"hybrid_override_pattern": "MEMEM*EMEMEM*EMEMEM*EMEMEM*EMEMEM*EMEMEMEM*EMEMEMEME",
"initializer_range": 0.02,
"intermediate_size": 1856,
"layer_norm_epsilon": 1e-05,
"mamba_head_dim": 64,
"mamba_hidden_act": "silu",
"mamba_num_heads": 64,
"mamba_proj_bias": false,
"mamba_ssm_cache_dtype": "float32",
"max_position_embeddings": 262144,
"mlp_bias": false,
"mlp_hidden_act": "relu2",
"model_type": "nemotron_h",
"moe_intermediate_size": 1856,
"moe_shared_expert_intermediate_size": 3712,
"n_group": 1,
"n_groups": 8,
"n_routed_experts": 128,
"n_shared_experts": 1,
"norm_eps": 1e-05,
"norm_topk_prob": true,
"num_attention_heads": 32,
"num_experts_per_tok": 6,
"num_hidden_layers": 52,
"num_key_value_heads": 2,
"num_logits_to_keep": 1,
"pad_token_id": 0,
"partial_rotary_factor": 1.0,
"rescale_prenorm_residual": true,
"residual_in_fp32": false,
"rope_theta": 10000,
"routed_scaling_factor": 2.5,
"sliding_window": null,
"ssm_state_size": 128,
"tie_word_embeddings": false,
"time_step_floor": 0.0001,
"time_step_limit": [
0.0,
10000000000.0
],
"time_step_max": 0.1,
"time_step_min": 0.001,
"topk_group": 1,
"transformers_version": "4.57.6",
"use_bias": false,
"use_cache": true,
"use_conv_bias": true,
"use_mamba_kernels": true,
"vocab_size": 131072
} |