Instructions to use maidacundo/open-mythos-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use maidacundo/open-mythos-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="maidacundo/open-mythos-tiny")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("maidacundo/open-mythos-tiny", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use maidacundo/open-mythos-tiny with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "maidacundo/open-mythos-tiny" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "maidacundo/open-mythos-tiny", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/maidacundo/open-mythos-tiny
- SGLang
How to use maidacundo/open-mythos-tiny 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 "maidacundo/open-mythos-tiny" \ --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": "maidacundo/open-mythos-tiny", "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 "maidacundo/open-mythos-tiny" \ --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": "maidacundo/open-mythos-tiny", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use maidacundo/open-mythos-tiny with Docker Model Runner:
docker model run hf.co/maidacundo/open-mythos-tiny
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ab9e7c1 | 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 | {
"architectures": [
"OpenMythosForCausalLM"
],
"bias": false,
"block_size": 256,
"bos_token_id": 1,
"dtype": "float32",
"effective_expected_depth": 10,
"eos_token_id": 2,
"head_dim": 64,
"init_values": {
"embed_scale": 16.0,
"embedding": 0.03952847075210474,
"out_proj": 0.008838834764831844,
"std": 0.03952847075210474
},
"injection_type": "lti",
"intermediate_size": 1024,
"kv_lora_rank": 512,
"mean_backprop_depth": 2,
"mean_recurrence": 4,
"model_type": "open_mythos",
"moe_intermediate_size": 1024,
"moe_top_k": 2,
"n_embd": 256,
"n_heads": 4,
"n_kv_heads": 4,
"n_layers": 4,
"n_layers_in_coda": 1,
"n_layers_in_prelude": 1,
"n_layers_in_recurrent_block": 2,
"n_routed_experts": 8,
"n_shared_experts": 2,
"norm_eps": 1e-06,
"num_key_value_heads": 4,
"pad_token_id": 0,
"q_lora_rank": 1536,
"qk_bias": true,
"rope_base": 50000.0,
"rope_head_dim": 64,
"state_init": "like-init",
"tie_embeddings": true,
"tie_word_embeddings": true,
"transformers_version": "5.6.0",
"use_cache": false,
"use_mla": false,
"use_moe": false,
"vocab_size": 50257
}
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