Text Generation
Transformers
Safetensors
English
huginn_raven
code
math
reasoning
llm
conversational
custom_code
Instructions to use tomg-group-umd/step-00011904-recurrence_full_512_0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tomg-group-umd/step-00011904-recurrence_full_512_0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="tomg-group-umd/step-00011904-recurrence_full_512_0", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("tomg-group-umd/step-00011904-recurrence_full_512_0", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use tomg-group-umd/step-00011904-recurrence_full_512_0 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tomg-group-umd/step-00011904-recurrence_full_512_0" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tomg-group-umd/step-00011904-recurrence_full_512_0", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/tomg-group-umd/step-00011904-recurrence_full_512_0
- SGLang
How to use tomg-group-umd/step-00011904-recurrence_full_512_0 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 "tomg-group-umd/step-00011904-recurrence_full_512_0" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tomg-group-umd/step-00011904-recurrence_full_512_0", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "tomg-group-umd/step-00011904-recurrence_full_512_0" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tomg-group-umd/step-00011904-recurrence_full_512_0", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use tomg-group-umd/step-00011904-recurrence_full_512_0 with Docker Model Runner:
docker model run hf.co/tomg-group-umd/step-00011904-recurrence_full_512_0
File size: 1,372 Bytes
fef90c9 | 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 | {
"activation_checkpoint_impl": "per-iteration",
"architecture_class_name": "RecurrentGPT",
"architectures": [
"RavenForCausalLM"
],
"auto_map": {
"AutoConfig": "raven_config_minimal.RavenConfig",
"AutoModelForCausalLM": "raven_modeling_minimal.RavenForCausalLM"
},
"bias": false,
"block_class_name": "SandwichBlock",
"block_size": 4096,
"effective_expected_depth": 132,
"head_dim": 96,
"init_orthogonal": false,
"init_strategy": "takase",
"init_values": {
"embed_scale": 72.6636084983398,
"embedding": 0.008703882797784892,
"out_proj": 0.0005356869554443541,
"std": 0.008703882797784892
},
"injection_type": "linear",
"intermediate_size": 17920,
"mean_backprop_depth": 8,
"mean_recurrence": 32,
"mlp_class_name": "GatedMLP",
"model_type": "huginn_raven",
"n_embd": 5280,
"n_heads": 55,
"n_layers": 8,
"n_layers_in_coda": 2,
"n_layers_in_prelude": 2,
"n_layers_in_recurrent_block": 4,
"nonlin_name": "SiLU",
"norm_class_name": "RMSNorm_llama",
"norm_eps": 1e-06,
"num_key_value_heads": 55,
"padded_vocab_size": 65536,
"padding_multiple": 4096,
"qk_bias": true,
"rope_base": 50000,
"sampling_scheme": "poisson-lognormal-filling",
"state_init": "like-init",
"tie_embeddings": true,
"torch_dtype": "float32",
"transformers_version": "4.44.2",
"vocab_size": 65536
}
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