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"} ] }'
| { | |
| "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 | |
| } |