Text Generation
Transformers
Safetensors
scrapegoat
dual-track
parallel-attention
Mixture of Experts
kda
quantile-balancing
Instructions to use scrapegoat/Scrapegoat-Tiny-Coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use scrapegoat/Scrapegoat-Tiny-Coder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="scrapegoat/Scrapegoat-Tiny-Coder")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("scrapegoat/Scrapegoat-Tiny-Coder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use scrapegoat/Scrapegoat-Tiny-Coder with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "scrapegoat/Scrapegoat-Tiny-Coder" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "scrapegoat/Scrapegoat-Tiny-Coder", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/scrapegoat/Scrapegoat-Tiny-Coder
- SGLang
How to use scrapegoat/Scrapegoat-Tiny-Coder 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 "scrapegoat/Scrapegoat-Tiny-Coder" \ --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": "scrapegoat/Scrapegoat-Tiny-Coder", "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 "scrapegoat/Scrapegoat-Tiny-Coder" \ --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": "scrapegoat/Scrapegoat-Tiny-Coder", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use scrapegoat/Scrapegoat-Tiny-Coder with Docker Model Runner:
docker model run hf.co/scrapegoat/Scrapegoat-Tiny-Coder
| { | |
| "_name_or_path": "ScrapeGoat-v1", | |
| "model_type": "scrapegoat", | |
| "vocab_size": 248320, | |
| "hidden_size": 4096, | |
| "num_hidden_layers": 81, | |
| "hidden_act": "silu", | |
| "max_position_embeddings": 262144, | |
| "initializer_range": 0.02, | |
| "rms_norm_eps": 1e-06, | |
| "use_cache": true, | |
| "pad_token_id": 0, | |
| "bos_token_id": 1, | |
| "eos_token_id": 2, | |
| "tie_word_embeddings": false, | |
| "rope_theta": 10000.0, | |
| "rope_scaling": null, | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "track_a_num_attention_heads": 32, | |
| "track_a_num_key_value_heads": 2, | |
| "track_a_head_dim": 256, | |
| "track_a_num_experts": 512, | |
| "track_a_moe_intermediate_size": 1024, | |
| "track_b_num_attention_heads": 64, | |
| "track_b_num_key_value_heads": 8, | |
| "track_b_head_dim": 128, | |
| "track_b_num_experts": 192, | |
| "num_experts": 704, | |
| "track_b_moe_intermediate_size": 1536, | |
| "track_b_intermediate_size": 13312, | |
| "num_experts_per_tok": 8, | |
| "output_router_logits": false, | |
| "router_aux_loss_coef": 0.001, | |
| "kda_head_dim": 256, | |
| "kda_conv_kernel": 3, | |
| "kda_gqa_layers": [ | |
| 0, | |
| 4, | |
| 8, | |
| 12, | |
| 16, | |
| 20, | |
| 24, | |
| 28, | |
| 32, | |
| 36, | |
| 40, | |
| 44, | |
| 48, | |
| 52, | |
| 56, | |
| 60, | |
| 64, | |
| 68, | |
| 72, | |
| 76, | |
| 80 | |
| ], | |
| "quantile_balancing": true, | |
| "qb_iterations": 5, | |
| "attn_residual": false, | |
| "attn_res_blocks": 8, | |
| "stable_moe_stage": 1, | |
| "mom_enabled": true, | |
| "mom_num_memories": 4, | |
| "mom_active_memories": 2, | |
| "mom_shared_memory": true, | |
| "mom_load_balancing": true, | |
| "stable_moe_r3": false, | |
| "stable_moe_r3_cache": true, | |
| "dspark_block_size": 6, | |
| "dspark_noise_token_id": 0, | |
| "dspark_target_layer_ids": [], | |
| "dspark_markov_rank": 256, | |
| "num_attention_heads": 32, | |
| "num_key_value_heads": 8, | |
| "architectures": [ | |
| "ScrapeGoatForCausalLM" | |
| ], | |
| "return_dict": true | |
| } |