Instructions to use mlx-community/Falcon-H1-Tiny-R-0.6B-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/Falcon-H1-Tiny-R-0.6B-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("mlx-community/Falcon-H1-Tiny-R-0.6B-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 mlx-community/Falcon-H1-Tiny-R-0.6B-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 "mlx-community/Falcon-H1-Tiny-R-0.6B-8bit"
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": "mlx-community/Falcon-H1-Tiny-R-0.6B-8bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use mlx-community/Falcon-H1-Tiny-R-0.6B-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 "mlx-community/Falcon-H1-Tiny-R-0.6B-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 mlx-community/Falcon-H1-Tiny-R-0.6B-8bit
Run Hermes
hermes
- MLX LM
How to use mlx-community/Falcon-H1-Tiny-R-0.6B-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 "mlx-community/Falcon-H1-Tiny-R-0.6B-8bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "mlx-community/Falcon-H1-Tiny-R-0.6B-8bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mlx-community/Falcon-H1-Tiny-R-0.6B-8bit", "messages": [ {"role": "user", "content": "Hello"} ] }'
File size: 2,004 Bytes
681495c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 | {
"architectures": [
"FalconH1ForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"attention_in_multiplier": 1.0,
"attention_out_multiplier": 1.0,
"attn_layer_indices": null,
"bos_token_id": 1,
"dtype": "bfloat16",
"embedding_multiplier": 1.0,
"eos_token_id": [
11,
228
],
"expansion_factor": 2.0,
"head_dim": 64,
"hidden_act": "silu",
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 2048,
"key_multiplier": 0.03125,
"lm_head_multiplier": 1.0,
"mamba_chunk_size": 128,
"mamba_conv_bias": true,
"mamba_d_conv": 4,
"mamba_d_head": 64,
"mamba_d_ssm": 1536,
"mamba_d_state": 128,
"mamba_expand": 2,
"mamba_n_groups": 1,
"mamba_n_heads": 24,
"mamba_norm_before_gate": false,
"mamba_proj_bias": false,
"mamba_rms_norm": false,
"mamba_use_mlp": true,
"max_position_embeddings": 262144,
"mlp_bias": false,
"mlp_expansion_factor": 8,
"mlp_multipliers": [
1.0,
1.0
],
"model_type": "falcon_h1",
"num_attention_heads": 8,
"num_hidden_layers": 44,
"num_key_value_heads": 2,
"num_logits_to_keep": 1,
"pad_token_id": 0,
"projectors_bias": false,
"quantization": {
"group_size": 64,
"bits": 8,
"mode": "affine"
},
"quantization_config": {
"group_size": 64,
"bits": 8,
"mode": "affine"
},
"rms_norm_eps": 1e-05,
"rope_scaling": null,
"rope_theta": 100000000000.0,
"sliding_window": null,
"ssm_in_multiplier": 1.0,
"ssm_multipliers": [
1.0,
1.0,
1.0,
1.0,
0.03125
],
"ssm_out_multiplier": 1.0,
"tie_word_embeddings": false,
"time_step_floor": 0.0001,
"time_step_max": 0.1,
"time_step_min": 0.001,
"time_step_rank": "auto",
"transformers_version": "4.57.0",
"use_cache": true,
"vocab_size": 32768
} |