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 Settings
- 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
Training in progress, step 10, checkpoint
Browse files- last-checkpoint/config.json +50 -0
- last-checkpoint/generation_config.json +9 -0
- last-checkpoint/model.safetensors +3 -0
- last-checkpoint/optimizer.pt +3 -0
- last-checkpoint/rng_state.pth +3 -0
- last-checkpoint/scheduler.pt +3 -0
- last-checkpoint/trainer_state.json +104 -0
- last-checkpoint/training_args.bin +3 -0
last-checkpoint/config.json
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{
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"architectures": [
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"OpenMythosForCausalLM"
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],
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"bias": false,
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"block_size": 256,
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| 7 |
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"bos_token_id": 1,
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"dtype": "float32",
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| 9 |
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"effective_expected_depth": 10,
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"eos_token_id": 2,
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"head_dim": 64,
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"init_values": {
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"embed_scale": 16.0,
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"embedding": 0.03952847075210474,
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"out_proj": 0.008838834764831844,
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"std": 0.03952847075210474
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},
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"injection_type": "lti",
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| 19 |
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"intermediate_size": 1024,
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| 20 |
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"kv_lora_rank": 512,
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| 21 |
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"mean_backprop_depth": 2,
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| 22 |
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"mean_recurrence": 4,
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"model_type": "open_mythos",
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"moe_intermediate_size": 1024,
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| 25 |
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"moe_top_k": 2,
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"n_embd": 256,
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"n_heads": 4,
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"n_kv_heads": 4,
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"n_layers": 4,
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"n_layers_in_coda": 1,
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"n_layers_in_prelude": 1,
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| 32 |
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"n_layers_in_recurrent_block": 2,
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"n_routed_experts": 8,
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"n_shared_experts": 2,
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"norm_eps": 1e-06,
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"num_key_value_heads": 4,
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"pad_token_id": 0,
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"q_lora_rank": 1536,
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"qk_bias": true,
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"rope_base": 50000.0,
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"rope_head_dim": 64,
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"state_init": "like-init",
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"tie_embeddings": true,
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| 44 |
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"tie_word_embeddings": true,
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"transformers_version": "5.6.0",
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"use_cache": false,
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"use_mla": false,
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| 48 |
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"use_moe": false,
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| 49 |
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"vocab_size": 50257
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}
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last-checkpoint/generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"output_attentions": false,
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"output_hidden_states": false,
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"pad_token_id": 0,
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"transformers_version": "5.6.0"
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}
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last-checkpoint/model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:6cc1acb58f6552f17f47c9e79c248b5766ef129b9cc7004d0289960bcfa126ad
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size 68602152
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last-checkpoint/optimizer.pt
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version https://git-lfs.github.com/spec/v1
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size 137100235
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last-checkpoint/rng_state.pth
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version https://git-lfs.github.com/spec/v1
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size 14455
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last-checkpoint/scheduler.pt
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version https://git-lfs.github.com/spec/v1
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size 1465
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last-checkpoint/trainer_state.json
ADDED
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"logging_steps": 1,
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}
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last-checkpoint/training_args.bin
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version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:6e7be4b80f34efa8c6ffa736724ae3152fa29c25aa950c50b57dfee4c09cee52
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| 3 |
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size 5329
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