Text Generation
PEFT
Safetensors
Transformers
lora
unsloth
gemma
gemma-4
tool-calling
function-calling
agent
coding
code-generation
conversational
Instructions to use TheOneWhoWill/Coding-Monkey-Gemma with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use TheOneWhoWill/Coding-Monkey-Gemma with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-4-12B-it") model = PeftModel.from_pretrained(base_model, "TheOneWhoWill/Coding-Monkey-Gemma") - Transformers
How to use TheOneWhoWill/Coding-Monkey-Gemma with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TheOneWhoWill/Coding-Monkey-Gemma") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("TheOneWhoWill/Coding-Monkey-Gemma", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use TheOneWhoWill/Coding-Monkey-Gemma with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TheOneWhoWill/Coding-Monkey-Gemma" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TheOneWhoWill/Coding-Monkey-Gemma", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/TheOneWhoWill/Coding-Monkey-Gemma
- SGLang
How to use TheOneWhoWill/Coding-Monkey-Gemma 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 "TheOneWhoWill/Coding-Monkey-Gemma" \ --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": "TheOneWhoWill/Coding-Monkey-Gemma", "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 "TheOneWhoWill/Coding-Monkey-Gemma" \ --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": "TheOneWhoWill/Coding-Monkey-Gemma", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Desktop
- Docker Model Runner
How to use TheOneWhoWill/Coding-Monkey-Gemma with Docker Model Runner:
docker model run hf.co/TheOneWhoWill/Coding-Monkey-Gemma
File size: 3,575 Bytes
c3c24f4 | 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 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 | {
"audio_token": "<|audio|>",
"backend": "tokenizers",
"boa_token": "<|audio>",
"boi_token": "<|image>",
"bos_token": "<bos>",
"eoa_token": "<audio|>",
"eoc_token": "<channel|>",
"eoi_token": "<image|>",
"eos_token": "<eos>",
"eot_token": "<turn|>",
"escape_token": "<|\"|>",
"etc_token": "<tool_call|>",
"etd_token": "<tool|>",
"etr_token": "<tool_response|>",
"extra_special_tokens": [],
"image_token": "<|image|>",
"is_local": false,
"local_files_only": false,
"mask_token": "<mask>",
"model_max_length": 1000000000000000019884624838656,
"model_specific_special_tokens": {
"audio_token": "<|audio|>",
"boa_token": "<|audio>",
"boi_token": "<|image>",
"eoa_token": "<audio|>",
"eoc_token": "<channel|>",
"eoi_token": "<image|>",
"eot_token": "<turn|>",
"escape_token": "<|\"|>",
"etc_token": "<tool_call|>",
"etd_token": "<tool|>",
"etr_token": "<tool_response|>",
"image_token": "<|image|>",
"soc_token": "<|channel>",
"sot_token": "<|turn>",
"stc_token": "<|tool_call>",
"std_token": "<|tool>",
"str_token": "<|tool_response>",
"think_token": "<|think|>"
},
"pad_token": "<pad>",
"padding_side": "right",
"processor_class": "Gemma4UnifiedProcessor",
"response_schema": {
"properties": {
"content": {
"type": "string"
},
"role": {
"const": "assistant"
},
"thinking": {
"type": "string"
},
"tool_calls": {
"items": {
"properties": {
"function": {
"properties": {
"arguments": {
"additionalProperties": {},
"type": "object",
"x-parser": "gemma4-tool-call"
},
"name": {
"type": "string"
}
},
"type": "object",
"x-regex": "call\\:(?P<name>\\w+)(?P<arguments>\\{.*\\})"
},
"type": {
"const": "function"
}
},
"type": "object"
},
"type": "array",
"x-regex-iterator": "<\\|tool_call>(.*?)<tool_call\\|>"
}
},
"type": "object",
"x-regex": "(\\<\\|channel\\>thought\\n(?P<thinking>.*?)\\<channel\\|\\>)?(?P<tool_calls>\\<\\|tool_call\\>.*\\<tool_call\\|\\>)?(?P<content>(?:(?!\\<turn\\|\\>)(?!\\<\\|tool_response\\>).)+)?(?:\\<turn\\|\\>|\\<\\|tool_response\\>)?"
},
"response_template": {
"defaults": {
"role": "assistant"
},
"fields": {
"content": {
"__type": "AddedToken",
"content": "text",
"lstrip": false,
"normalized": true,
"rstrip": false,
"single_word": false,
"special": false
},
"thinking": {
"__type": "AddedToken",
"content": "text",
"lstrip": false,
"normalized": true,
"rstrip": false,
"single_word": false,
"special": false
},
"tool_calls": {
"__type": "AddedToken",
"content": "json",
"lstrip": false,
"normalized": true,
"rstrip": false,
"single_word": false,
"special": false
}
},
"start_anchor": [
"<|turn>model\n",
"<tool_response|>"
]
},
"soc_token": "<|channel>",
"sot_token": "<|turn>",
"stc_token": "<|tool_call>",
"std_token": "<|tool>",
"str_token": "<|tool_response>",
"think_token": "<|think|>",
"tokenizer_class": "GemmaTokenizer",
"unk_token": "<unk>"
}
|