Instructions to use reaperdoesntknow/echo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use reaperdoesntknow/echo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="reaperdoesntknow/echo", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("reaperdoesntknow/echo", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use reaperdoesntknow/echo with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "reaperdoesntknow/echo" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "reaperdoesntknow/echo", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/reaperdoesntknow/echo
- SGLang
How to use reaperdoesntknow/echo 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 "reaperdoesntknow/echo" \ --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": "reaperdoesntknow/echo", "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 "reaperdoesntknow/echo" \ --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": "reaperdoesntknow/echo", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use reaperdoesntknow/echo with Docker Model Runner:
docker model run hf.co/reaperdoesntknow/echo
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77d1918 03ae013 77d1918 | 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 | {
"architectures": [
"TAMELM"
],
"attention_backend": "flex",
"attention_top_k": 2,
"auto_map": {
"AutoConfig": "modeling_tamelm.TAMEConfig",
"AutoModelForCausalLM": "modeling_tamelm.TAMELM"
},
"balance_coef": 0.01,
"bos_token_id": 1,
"depth_dim": 768,
"depth_gate_init": 0.01,
"depth_heads": 8,
"depth_mode": "attention",
"depth_slots": 16,
"dtype": "float32",
"embed_dim": 1024,
"eos_token_id": 2,
"expert_backend": "grouped",
"expert_hidden_dim": 4096,
"expert_top_k": 2,
"graph_recent": 256,
"graph_top_r": 2,
"init_scheme": "v1",
"local_window": 256,
"loss_chunk_size": 256,
"max_seq_len": 1024,
"model_type": "tamelm_two_axis",
"n_kv_heads": 4,
"n_q_heads": 16,
"num_experts": 4,
"num_layers": 12,
"pad_token_id": 0,
"rms_norm_eps": 1e-06,
"rope_type": "warped",
"router_init_std": 0.01,
"strided_every": 4,
"strided_recent": 128,
"tie_word_embeddings": true,
"tile": 128,
"tokenizer_sha256": "0d93d8f943b1a6627d62395c2fd583894cfe577b8aca1ca946495f7abba50958",
"transformers_version": "5.5.0",
"use_cache": false,
"vocab_size": 49152
}
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