Instructions to use jc4rt/LFM2.5-1.2b-thinking-claude-creative_writing_fine_tune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use jc4rt/LFM2.5-1.2b-thinking-claude-creative_writing_fine_tune 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("jc4rt/LFM2.5-1.2b-thinking-claude-creative_writing_fine_tune") 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
- MLX LM
How to use jc4rt/LFM2.5-1.2b-thinking-claude-creative_writing_fine_tune with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "jc4rt/LFM2.5-1.2b-thinking-claude-creative_writing_fine_tune"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "jc4rt/LFM2.5-1.2b-thinking-claude-creative_writing_fine_tune" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jc4rt/LFM2.5-1.2b-thinking-claude-creative_writing_fine_tune", "messages": [ {"role": "user", "content": "Hello"} ] }'
File size: 1,522 Bytes
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"architectures": [
"Lfm2ForCausalLM"
],
"block_auto_adjust_ff_dim": true,
"block_dim": 2048,
"block_ff_dim": 12288,
"block_ffn_dim_multiplier": 1.0,
"block_mlp_init_scale": 1.0,
"block_multiple_of": 256,
"block_norm_eps": 1e-05,
"block_out_init_scale": 1.0,
"block_use_swiglu": true,
"block_use_xavier_init": true,
"bos_token_id": 1,
"conv_L_cache": 3,
"conv_bias": false,
"conv_dim": 2048,
"conv_use_xavier_init": true,
"eos_token_id": 7,
"full_attn_idxs": null,
"hidden_size": 2048,
"initializer_range": 0.02,
"intermediate_size": 12288,
"layer_types": [
"conv",
"conv",
"full_attention",
"conv",
"conv",
"full_attention",
"conv",
"conv",
"full_attention",
"conv",
"full_attention",
"conv",
"full_attention",
"conv",
"full_attention",
"conv"
],
"max_position_embeddings": 128000,
"model_name": "LiquidAI/LFM2.5-1.2B-Thinking",
"model_type": "lfm2",
"norm_eps": 1e-05,
"num_attention_heads": 32,
"num_heads": 32,
"num_hidden_layers": 16,
"num_key_value_heads": 8,
"pad_token_id": 0,
"rope_parameters": {
"rope_theta": 1000000.0,
"rope_type": "default"
},
"tie_word_embeddings": true,
"torch_dtype": "float16",
"unsloth_version": "2026.4.4",
"use_cache": false,
"use_pos_enc": true,
"vocab_size": 65536
} |