Instructions to use Harley-ml/Dillion-1.2M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Harley-ml/Dillion-1.2M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Harley-ml/Dillion-1.2M")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Harley-ml/Dillion-1.2M") model = AutoModelForCausalLM.from_pretrained("Harley-ml/Dillion-1.2M") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Harley-ml/Dillion-1.2M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Harley-ml/Dillion-1.2M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Harley-ml/Dillion-1.2M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Harley-ml/Dillion-1.2M
- SGLang
How to use Harley-ml/Dillion-1.2M with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Harley-ml/Dillion-1.2M" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Harley-ml/Dillion-1.2M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Harley-ml/Dillion-1.2M" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Harley-ml/Dillion-1.2M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Harley-ml/Dillion-1.2M with Docker Model Runner:
docker model run hf.co/Harley-ml/Dillion-1.2M
File size: 1,246 Bytes
f025636 55ff9eb f025636 | 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 | {
"architectures": [
"Qwen3_5ForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": null,
"dtype": "float32",
"eos_token_id": null,
"head_dim": 24,
"hidden_act": "silu",
"hidden_size": 72,
"initializer_range": 0.02,
"intermediate_size": 288,
"layer_types": [
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention"
],
"linear_conv_kernel_dim": 4,
"linear_key_head_dim": 24,
"linear_num_key_heads": 3,
"linear_num_value_heads": 3,
"linear_value_head_dim": 24,
"max_position_embeddings": 384,
"model_type": "qwen3_5_text",
"num_attention_heads": 3,
"num_hidden_layers": 12,
"num_key_value_heads": 3,
"pad_token_id": null,
"partial_rotary_factor": 0.25,
"rms_norm_eps": 1e-06,
"rope_parameters": {
"partial_rotary_factor": 0.25,
"rope_theta": 10000.0,
"rope_type": "default"
},
"tie_word_embeddings": true,
"transformers_version": "5.8.1",
"use_cache": false,
"vocab_size": 3076
}
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