Instructions to use shailesh83/qwen-2.5-coder-ST 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 shailesh83/qwen-2.5-coder-ST 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 shailesh83/qwen-2.5-coder-ST:BF16 # Run inference directly in the terminal: llama cli -hf shailesh83/qwen-2.5-coder-ST:BF16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf shailesh83/qwen-2.5-coder-ST:BF16 # Run inference directly in the terminal: llama cli -hf shailesh83/qwen-2.5-coder-ST:BF16
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf shailesh83/qwen-2.5-coder-ST:BF16 # Run inference directly in the terminal: ./llama-cli -hf shailesh83/qwen-2.5-coder-ST:BF16
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf shailesh83/qwen-2.5-coder-ST:BF16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf shailesh83/qwen-2.5-coder-ST:BF16
Use Docker
docker model run hf.co/shailesh83/qwen-2.5-coder-ST:BF16
- LM Studio
- Jan
- Ollama
How to use shailesh83/qwen-2.5-coder-ST with Ollama:
ollama run hf.co/shailesh83/qwen-2.5-coder-ST:BF16
- Unsloth Studio
How to use shailesh83/qwen-2.5-coder-ST 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 shailesh83/qwen-2.5-coder-ST 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 shailesh83/qwen-2.5-coder-ST to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for shailesh83/qwen-2.5-coder-ST to start chatting
- Pi
How to use shailesh83/qwen-2.5-coder-ST with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf shailesh83/qwen-2.5-coder-ST:BF16
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "shailesh83/qwen-2.5-coder-ST:BF16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use shailesh83/qwen-2.5-coder-ST with Docker Model Runner:
docker model run hf.co/shailesh83/qwen-2.5-coder-ST:BF16
- Lemonade
How to use shailesh83/qwen-2.5-coder-ST with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull shailesh83/qwen-2.5-coder-ST:BF16
Run and chat with the model
lemonade run user.qwen-2.5-coder-ST-BF16
List all available models
lemonade list
- Hermes Agent
How to use shailesh83/qwen-2.5-coder-ST with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf shailesh83/qwen-2.5-coder-ST:BF16
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 shailesh83/qwen-2.5-coder-ST:BF16
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use shailesh83/qwen-2.5-coder-ST with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf shailesh83/qwen-2.5-coder-ST:BF16
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 "shailesh83/qwen-2.5-coder-ST:BF16" \ --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"
File size: 1,529 Bytes
63313af | 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 | # Qwen2.5 Coder Instruct (tool-calling) under Ollama
# swap the FROM line for your quant:
# FROM ./qwen-2.5-coder-toolcall-q8.gguf
FROM ./qwen-2.5-coder-toolcall-bf16.gguf
PARAMETER num_ctx 8192
PARAMETER temperature 0.2
PARAMETER top_p 0.9
PARAMETER repeat_penalty 1.1
PARAMETER stop "<|im_end|>"
SYSTEM """
You are a precise CODESYS expert. Use tools to create/modify IEC 61131-3 Structured Text (ST) when appropriate.
When calling a tool, you MUST return ONLY a single JSON object inside <tool_call> tags, exactly as:
<tool_call>
{"name": "<function-name>", "arguments": { ... }}
</tool_call>
No extra prose, no code fences, no trailing text during a tool call. If no tool is needed, answer normally.
"""
# Qwen-style prompt with a Tools block. Important: render each tool as {"type":"function","function": ...}
# and instruct the model to return the call inside <tool_call>…</tool_call>.
TEMPLATE """{{- if .System -}}<|im_start|>system
{{ .System }}{{ if .Tools }}
# Tools
You may call one or more functions to assist with the user task.
Function signatures are provided inside <tools></tools>:
<tools>
{{- range .Tools }}
{"type": "function", "function": {{ .Function }}}
{{- end }}
</tools>
To call a function, return ONLY a single JSON object inside <tool_call> tags:
<tool_call>
{"name": <function-name>, "arguments": <args-json-object>}
</tool_call>
{{ end }}
<|im_end|>
{{- end -}}
{{- range .Messages -}}
<|im_start|>{{ .Role }}
{{ .Content }}<|im_end|>
{{- end -}}
<|im_start|>assistant
{{ .Response }}"""
|