Safetensors
GGUF
English
qwen2
unsloth
qwen
qwen2.5
code-generation
tool-calling
agent
conversational
Instructions to use IldaLabs/OpenCodeAgent-0.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use IldaLabs/OpenCodeAgent-0.5B with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf IldaLabs/OpenCodeAgent-0.5B:Q4_K_M # Run inference directly in the terminal: llama cli -hf IldaLabs/OpenCodeAgent-0.5B:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf IldaLabs/OpenCodeAgent-0.5B:Q4_K_M # Run inference directly in the terminal: llama cli -hf IldaLabs/OpenCodeAgent-0.5B:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf IldaLabs/OpenCodeAgent-0.5B:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf IldaLabs/OpenCodeAgent-0.5B:Q4_K_M
Use Docker
docker model run hf.co/IldaLabs/OpenCodeAgent-0.5B:Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use IldaLabs/OpenCodeAgent-0.5B with Ollama:
ollama run hf.co/IldaLabs/OpenCodeAgent-0.5B:Q4_K_M
- Unsloth Desktop
- Pi
How to use IldaLabs/OpenCodeAgent-0.5B with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf IldaLabs/OpenCodeAgent-0.5B:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "IldaLabs/OpenCodeAgent-0.5B:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use IldaLabs/OpenCodeAgent-0.5B with Docker Model Runner:
docker model run hf.co/IldaLabs/OpenCodeAgent-0.5B:Q4_K_M
- Lemonade
How to use IldaLabs/OpenCodeAgent-0.5B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull IldaLabs/OpenCodeAgent-0.5B:Q4_K_M
Run and chat with the model
lemonade run user.OpenCodeAgent-0.5B-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use IldaLabs/OpenCodeAgent-0.5B with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf IldaLabs/OpenCodeAgent-0.5B:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default IldaLabs/OpenCodeAgent-0.5B:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use IldaLabs/OpenCodeAgent-0.5B with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf IldaLabs/OpenCodeAgent-0.5B:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "IldaLabs/OpenCodeAgent-0.5B:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Trained with Unsloth
Browse filesUpload model trained with Unsloth 2x faster
- .gitattributes +1 -0
- Modelfile +57 -0
- Qwen2.5-0.5B-Instruct.Q4_K_M.gguf +3 -0
- README.md +14 -13
- config.json +58 -0
.gitattributes
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*.zst filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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Qwen2.5-0.5B-Instruct.Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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Modelfile
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FROM Qwen2.5-0.5B-Instruct.Q4_K_M.gguf
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TEMPLATE """{{- if .Messages }}
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{{- if or .System .Tools }}<|im_start|>system
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{{- if .System }}
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{{ .System }}
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{{- end }}
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{{- if .Tools }}
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# Tools
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You may call one or more functions to assist with the user query.
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You are provided with function signatures within <tools></tools> XML tags:
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<tools>
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{{- range .Tools }}
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{"type": "function", "function": {{ .Function }}}
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{{- end }}
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</tools>
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For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
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<tool_call>
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{"name": <function-name>, "arguments": <args-json-object>}
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</tool_call>
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{{- end }}<|im_end|>
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{{ end }}
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{{- range $i, $_ := .Messages }}
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{{- $last := eq (len (slice $.Messages $i)) 1 -}}
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{{- if eq .Role "user" }}<|im_start|>user
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{{ .Content }}<|im_end|>
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{{ else if eq .Role "assistant" }}<|im_start|>assistant
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{{ if .Content }}{{ .Content }}
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{{- else if .ToolCalls }}<tool_call>
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{{ range .ToolCalls }}{"name": "{{ .Function.Name }}", "arguments": {{ .Function.Arguments }}}
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{{ end }}</tool_call>
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{{- end }}{{ if not $last }}<|im_end|>
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{{ end }}
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{{- else if eq .Role "tool" }}<|im_start|>user
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<tool_response>
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{{ .Content }}
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</tool_response><|im_end|>
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{{ end }}
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{{- if and (ne .Role "assistant") $last }}<|im_start|>assistant
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{{ end }}
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{{- end }}
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{{- else }}
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{{- if .System }}<|im_start|>system
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{{ .System }}<|im_end|>
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{{ end }}{{ if .Prompt }}<|im_start|>user
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{{ .Prompt }}<|im_end|>
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{{ end }}<|im_start|>assistant
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{{ end }}{{ .Response }}{{ if .Response }}<|im_end|>{{ end }}"""
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PARAMETER stop "<|im_end|>"
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PARAMETER stop "<|endoftext|>"
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PARAMETER temperature 1.5
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PARAMETER min_p 0.1
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SYSTEM """You are Qwen, created by Alibaba Cloud. You are a helpful assistant."""
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Qwen2.5-0.5B-Instruct.Q4_K_M.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:096b3e532a7bd42e531ebac96a7a47db83a5ce01c45f8a47c3ce8b968656e632
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size 397807648
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README.md
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---
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base_model: unsloth/Qwen2.5-0.5B-Instruct-unsloth-bnb-4bit
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tags:
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- unsloth
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license: apache-2.0
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language:
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---
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#
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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---
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tags:
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- gguf
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- llama.cpp
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- unsloth
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---
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# OpenCodeAgent-0.5B : GGUF
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This model was finetuned and converted to GGUF format using [Unsloth](https://github.com/unslothai/unsloth).
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**Example usage**:
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- For text only LLMs: `llama-cli -hf IldaLabs/OpenCodeAgent-0.5B --jinja`
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- For multimodal models: `llama-mtmd-cli -hf IldaLabs/OpenCodeAgent-0.5B --jinja`
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## Available Model files:
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- `Qwen2.5-0.5B-Instruct.Q4_K_M.gguf`
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## Ollama
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An Ollama Modelfile is included for easy deployment.
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This was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth)
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": null,
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"torch_dtype": "bfloat16",
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"eos_token_id": 151645,
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"hidden_act": "silu",
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"hidden_size": 896,
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"initializer_range": 0.02,
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"intermediate_size": 4864,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 32768,
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"max_window_layers": 21,
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"model_type": "qwen2",
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"num_attention_heads": 14,
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"num_hidden_layers": 24,
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"num_key_value_heads": 2,
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"pad_token_id": 151654,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
|
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"rope_theta": 1000000.0,
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"rope_type": "default"
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},
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"sliding_window": null,
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"tie_word_embeddings": true,
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"transformers_version": "5.17.0",
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"unsloth_fixed": true,
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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