Instructions to use AnTechAI/qc-thamdo-updated-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use AnTechAI/qc-thamdo-updated-v1 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen3-4B-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "AnTechAI/qc-thamdo-updated-v1") - Transformers
How to use AnTechAI/qc-thamdo-updated-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AnTechAI/qc-thamdo-updated-v1") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AnTechAI/qc-thamdo-updated-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use AnTechAI/qc-thamdo-updated-v1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AnTechAI/qc-thamdo-updated-v1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AnTechAI/qc-thamdo-updated-v1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/AnTechAI/qc-thamdo-updated-v1
- SGLang
How to use AnTechAI/qc-thamdo-updated-v1 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 "AnTechAI/qc-thamdo-updated-v1" \ --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": "AnTechAI/qc-thamdo-updated-v1", "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 "AnTechAI/qc-thamdo-updated-v1" \ --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": "AnTechAI/qc-thamdo-updated-v1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Studio
How to use AnTechAI/qc-thamdo-updated-v1 with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for AnTechAI/qc-thamdo-updated-v1 to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for AnTechAI/qc-thamdo-updated-v1 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for AnTechAI/qc-thamdo-updated-v1 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="AnTechAI/qc-thamdo-updated-v1", max_seq_length=2048, ) - Docker Model Runner
How to use AnTechAI/qc-thamdo-updated-v1 with Docker Model Runner:
docker model run hf.co/AnTechAI/qc-thamdo-updated-v1
Trần Quốc An commited on
Upload folder using huggingface_hub
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| 78 |
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|
| 79 |
},
|
| 80 |
"151651": {
|
| 81 |
"content": "<|quad_end|>",
|
| 82 |
+
"single_word": false,
|
| 83 |
"lstrip": false,
|
|
|
|
| 84 |
"rstrip": false,
|
| 85 |
+
"normalized": false,
|
| 86 |
"special": true
|
| 87 |
},
|
| 88 |
"151652": {
|
| 89 |
"content": "<|vision_start|>",
|
| 90 |
+
"single_word": false,
|
| 91 |
"lstrip": false,
|
|
|
|
| 92 |
"rstrip": false,
|
| 93 |
+
"normalized": false,
|
| 94 |
"special": true
|
| 95 |
},
|
| 96 |
"151653": {
|
| 97 |
"content": "<|vision_end|>",
|
| 98 |
+
"single_word": false,
|
| 99 |
"lstrip": false,
|
|
|
|
| 100 |
"rstrip": false,
|
| 101 |
+
"normalized": false,
|
| 102 |
"special": true
|
| 103 |
},
|
| 104 |
"151654": {
|
| 105 |
"content": "<|vision_pad|>",
|
| 106 |
+
"single_word": false,
|
| 107 |
"lstrip": false,
|
|
|
|
| 108 |
"rstrip": false,
|
| 109 |
+
"normalized": false,
|
| 110 |
"special": true
|
| 111 |
},
|
| 112 |
"151655": {
|
| 113 |
"content": "<|image_pad|>",
|
| 114 |
+
"single_word": false,
|
| 115 |
"lstrip": false,
|
|
|
|
| 116 |
"rstrip": false,
|
| 117 |
+
"normalized": false,
|
| 118 |
"special": true
|
| 119 |
},
|
| 120 |
"151656": {
|
| 121 |
"content": "<|video_pad|>",
|
| 122 |
+
"single_word": false,
|
| 123 |
"lstrip": false,
|
|
|
|
| 124 |
"rstrip": false,
|
| 125 |
+
"normalized": false,
|
| 126 |
"special": true
|
| 127 |
},
|
| 128 |
"151657": {
|
| 129 |
"content": "<tool_call>",
|
| 130 |
+
"single_word": false,
|
| 131 |
"lstrip": false,
|
|
|
|
| 132 |
"rstrip": false,
|
| 133 |
+
"normalized": false,
|
| 134 |
"special": false
|
| 135 |
},
|
| 136 |
"151658": {
|
| 137 |
"content": "</tool_call>",
|
| 138 |
+
"single_word": false,
|
| 139 |
"lstrip": false,
|
|
|
|
| 140 |
"rstrip": false,
|
| 141 |
+
"normalized": false,
|
| 142 |
"special": false
|
| 143 |
},
|
| 144 |
"151659": {
|
| 145 |
"content": "<|fim_prefix|>",
|
| 146 |
+
"single_word": false,
|
| 147 |
"lstrip": false,
|
|
|
|
| 148 |
"rstrip": false,
|
| 149 |
+
"normalized": false,
|
| 150 |
"special": false
|
| 151 |
},
|
| 152 |
"151660": {
|
| 153 |
"content": "<|fim_middle|>",
|
| 154 |
+
"single_word": false,
|
| 155 |
"lstrip": false,
|
|
|
|
| 156 |
"rstrip": false,
|
| 157 |
+
"normalized": false,
|
| 158 |
"special": false
|
| 159 |
},
|
| 160 |
"151661": {
|
| 161 |
"content": "<|fim_suffix|>",
|
| 162 |
+
"single_word": false,
|
| 163 |
"lstrip": false,
|
|
|
|
| 164 |
"rstrip": false,
|
| 165 |
+
"normalized": false,
|
| 166 |
"special": false
|
| 167 |
},
|
| 168 |
"151662": {
|
| 169 |
"content": "<|fim_pad|>",
|
| 170 |
+
"single_word": false,
|
| 171 |
"lstrip": false,
|
|
|
|
| 172 |
"rstrip": false,
|
| 173 |
+
"normalized": false,
|
| 174 |
"special": false
|
| 175 |
},
|
| 176 |
"151663": {
|
| 177 |
"content": "<|repo_name|>",
|
| 178 |
+
"single_word": false,
|
| 179 |
"lstrip": false,
|
|
|
|
| 180 |
"rstrip": false,
|
| 181 |
+
"normalized": false,
|
| 182 |
"special": false
|
| 183 |
},
|
| 184 |
"151664": {
|
| 185 |
"content": "<|file_sep|>",
|
| 186 |
+
"single_word": false,
|
| 187 |
"lstrip": false,
|
|
|
|
| 188 |
"rstrip": false,
|
| 189 |
+
"normalized": false,
|
| 190 |
"special": false
|
| 191 |
},
|
| 192 |
"151665": {
|
| 193 |
"content": "<tool_response>",
|
| 194 |
+
"single_word": false,
|
| 195 |
"lstrip": false,
|
|
|
|
| 196 |
"rstrip": false,
|
| 197 |
+
"normalized": false,
|
| 198 |
"special": false
|
| 199 |
},
|
| 200 |
"151666": {
|
| 201 |
"content": "</tool_response>",
|
| 202 |
+
"single_word": false,
|
| 203 |
"lstrip": false,
|
|
|
|
| 204 |
"rstrip": false,
|
| 205 |
+
"normalized": false,
|
| 206 |
"special": false
|
| 207 |
},
|
| 208 |
"151667": {
|
| 209 |
"content": "<think>",
|
| 210 |
+
"single_word": false,
|
| 211 |
"lstrip": false,
|
|
|
|
| 212 |
"rstrip": false,
|
| 213 |
+
"normalized": false,
|
| 214 |
"special": false
|
| 215 |
},
|
| 216 |
"151668": {
|
| 217 |
"content": "</think>",
|
| 218 |
+
"single_word": false,
|
| 219 |
"lstrip": false,
|
|
|
|
| 220 |
"rstrip": false,
|
| 221 |
+
"normalized": false,
|
| 222 |
"special": false
|
| 223 |
},
|
| 224 |
"151669": {
|
| 225 |
"content": "<|PAD_TOKEN|>",
|
| 226 |
+
"single_word": false,
|
| 227 |
"lstrip": false,
|
|
|
|
| 228 |
"rstrip": false,
|
| 229 |
+
"normalized": false,
|
| 230 |
"special": true
|
| 231 |
}
|
| 232 |
+
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 233 |
}
|