Text Generation
Transformers
Safetensors
English
bananamind2_pico
causal-lm
base-model
muon
xsa-refresh
custom-code
trust-remote-code
custom_code
Instructions to use Banaxi-Tech/pico-60 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Banaxi-Tech/pico-60 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Banaxi-Tech/pico-60", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Banaxi-Tech/pico-60", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Banaxi-Tech/pico-60 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Banaxi-Tech/pico-60" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Banaxi-Tech/pico-60", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Banaxi-Tech/pico-60
- SGLang
How to use Banaxi-Tech/pico-60 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 "Banaxi-Tech/pico-60" \ --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": "Banaxi-Tech/pico-60", "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 "Banaxi-Tech/pico-60" \ --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": "Banaxi-Tech/pico-60", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Banaxi-Tech/pico-60 with Docker Model Runner:
docker model run hf.co/Banaxi-Tech/pico-60
File size: 1,891 Bytes
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"parameters": 900002,
"architecture": {
"vocab_size": 384,
"hidden_size": 96,
"num_hidden_layers": 6,
"num_attention_heads": 6,
"num_key_value_heads": 2,
"head_dim": 16,
"intermediate_size": 380,
"refresh_layers": [
3,
5
],
"refresh_kernel_size": 9,
"refresh_alpha_init": 0.0,
"max_position_embeddings": 4096,
"rope_theta": 100000.0,
"rms_norm_eps": 1e-06,
"initializer_range": 0.02,
"use_cache": true,
"transformers_version": "5.15.0",
"architectures": null,
"output_hidden_states": false,
"return_dict": true,
"dtype": null,
"chunk_size_feed_forward": 0,
"is_encoder_decoder": false,
"id2label": {
"0": "LABEL_0",
"1": "LABEL_1"
},
"label2id": {
"LABEL_0": 0,
"LABEL_1": 1
},
"problem_type": null,
"_name_or_path": "",
"tie_word_embeddings": true,
"bos_token_id": 1,
"eos_token_id": 2,
"pad_token_id": 0,
"unk_token_id": 3,
"model_type": "bananamind2_pico",
"output_attentions": false
},
"training_percent": 60,
"step": 13464,
"total_steps": 22439,
"tokens_seen": 120003231744,
"target_tokens": 200000000000,
"trained_token_target": 199996473344,
"tokens_per_step": 8912896,
"local_batch": 544,
"effective_global_batch": 2176,
"world_size": 4,
"gpu_name": "NVIDIA H200",
"optimizer": "Muon + AdamW",
"muon_peak_lr": 0.07,
"adamw_peak_lr": 0.004,
"architecture_revision": "ee5739850035c878ce4d9fc29fc38b075987e03b",
"tokenizer_revision": "ee5739850035c878ce4d9fc29fc38b075987e03b",
"dataset_revisions": {
"fineweb_hq": "e58199cdd52438d94405df1a4d8630cc5f13bf84",
"cosmopedia_v2": "3ba9d605774198c5868892d7a8deda78031a781f"
},
"source_tokens": {
"fineweb_hq": 91998912512,
"cosmopedia_v2": 28004319232
},
"elapsed_seconds": 16407.752678394318
}
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