Text Generation
Transformers
Safetensors
English
spike_whale
image-feature-extraction
small-models
mla
jepa
experimental
custom_code
Instructions to use Quazim0t0/Byrne-86M-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Quazim0t0/Byrne-86M-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Quazim0t0/Byrne-86M-Base", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Quazim0t0/Byrne-86M-Base", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Quazim0t0/Byrne-86M-Base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Quazim0t0/Byrne-86M-Base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Quazim0t0/Byrne-86M-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Quazim0t0/Byrne-86M-Base
- SGLang
How to use Quazim0t0/Byrne-86M-Base 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 "Quazim0t0/Byrne-86M-Base" \ --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": "Quazim0t0/Byrne-86M-Base", "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 "Quazim0t0/Byrne-86M-Base" \ --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": "Quazim0t0/Byrne-86M-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Quazim0t0/Byrne-86M-Base with Docker Model Runner:
docker model run hf.co/Quazim0t0/Byrne-86M-Base
File size: 1,641 Bytes
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"architectures": [
"SpikeWhaleLM"
],
"attention_dropout": 0.0,
"bos_token_id": 2,
"dtype": "float32",
"engram_compress_dim": 32,
"engram_gate_init_bias": -1.0,
"engram_max_ngram": 3,
"engram_num_heads": 2,
"engram_table_size": 4096,
"eos_token_id": 3,
"hc_eps": 1e-06,
"hc_mult": 2,
"hc_sinkhorn_iters": 20,
"head_dim": 64,
"hidden_dropout": 0.0,
"hidden_size": 640,
"hrm_refine_dim": 128,
"hrm_refine_steps": 1,
"initializer_range": 0.02,
"max_position_embeddings": 4096,
"model_type": "spike_whale",
"moe_aux_loss_coef": 0.01,
"moe_intermediate_size": 2000,
"moe_layers": [],
"mtp_loss_weight": 0.3,
"n_routed_experts": 6,
"n_shared_experts": 1,
"nope_head_dim": 48,
"norm_topk_prob": true,
"num_attention_heads": 10,
"num_experts_per_tok": 2,
"num_hash_layers": 2,
"num_hidden_layers": 16,
"num_key_value_heads": 1,
"num_nextn_predict_layers": 1,
"o_lora_rank": 128,
"q_lora_rank": 128,
"qk_rope_head_dim": 16,
"rms_norm_eps": 1e-06,
"rope_theta": 10000.0,
"routed_scaling_factor": 1.0,
"scoring_func": "sqrtsoftplus",
"tie_word_embeddings": true,
"transformers_version": "5.8.0",
"use_derf": false,
"use_engram": true,
"use_hrm_refine": true,
"use_hyper_connections": true,
"use_moe": false,
"use_qk_norm": true,
"use_value_embed": false,
"use_xsa": true,
"vocab_size": 16512,
"zloss_coef": 0.0001,
"auto_map": {
"AutoConfig": "config.SpikeWhaleConfig",
"AutoModel": "model_v2.SpikeWhaleLM",
"AutoModelForCausalLM": "model_v2.SpikeWhaleLM"
}
} |