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
| { | |
| "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" | |
| } | |
| } |