Text Generation
GGUF
Safetensors
English
Turkish
plc
iec-61131-3
structured-text
code-generation
ollama
mikrodev
ALB
AdvanceLogicBuilder
MikrodevLogicStudio
advance-logic-builder
mikrodev-logicstudio
qwen2.5
conversational
Instructions to use Mikrodev/stcoder-qwen25-7b-gguf 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 Mikrodev/stcoder-qwen25-7b-gguf 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 Mikrodev/stcoder-qwen25-7b-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf Mikrodev/stcoder-qwen25-7b-gguf:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Mikrodev/stcoder-qwen25-7b-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf Mikrodev/stcoder-qwen25-7b-gguf:Q4_K_M
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 Mikrodev/stcoder-qwen25-7b-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Mikrodev/stcoder-qwen25-7b-gguf:Q4_K_M
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 Mikrodev/stcoder-qwen25-7b-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Mikrodev/stcoder-qwen25-7b-gguf:Q4_K_M
Use Docker
docker model run hf.co/Mikrodev/stcoder-qwen25-7b-gguf:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Mikrodev/stcoder-qwen25-7b-gguf with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Mikrodev/stcoder-qwen25-7b-gguf" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Mikrodev/stcoder-qwen25-7b-gguf", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Mikrodev/stcoder-qwen25-7b-gguf:Q4_K_M
- Ollama
How to use Mikrodev/stcoder-qwen25-7b-gguf with Ollama:
ollama run hf.co/Mikrodev/stcoder-qwen25-7b-gguf:Q4_K_M
- Unsloth Studio
How to use Mikrodev/stcoder-qwen25-7b-gguf 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 Mikrodev/stcoder-qwen25-7b-gguf 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 Mikrodev/stcoder-qwen25-7b-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Mikrodev/stcoder-qwen25-7b-gguf to start chatting
- Pi
How to use Mikrodev/stcoder-qwen25-7b-gguf with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Mikrodev/stcoder-qwen25-7b-gguf:Q4_K_M
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": "Mikrodev/stcoder-qwen25-7b-gguf:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Mikrodev/stcoder-qwen25-7b-gguf with Docker Model Runner:
docker model run hf.co/Mikrodev/stcoder-qwen25-7b-gguf:Q4_K_M
- Lemonade
How to use Mikrodev/stcoder-qwen25-7b-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Mikrodev/stcoder-qwen25-7b-gguf:Q4_K_M
Run and chat with the model
lemonade run user.stcoder-qwen25-7b-gguf-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use Mikrodev/stcoder-qwen25-7b-gguf with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Mikrodev/stcoder-qwen25-7b-gguf: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 Mikrodev/stcoder-qwen25-7b-gguf:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Mikrodev/stcoder-qwen25-7b-gguf with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Mikrodev/stcoder-qwen25-7b-gguf: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 "Mikrodev/stcoder-qwen25-7b-gguf: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"
Upload Modelfile with huggingface_hub
Browse files
Modelfile
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# 2) ollama create stcoder-qwen25-7b:<quant> -f Modelfile e.g. ollama create stcoder-qwen25-7b:q8_0 -f Modelfile
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# 3) ollama run stcoder-qwen25-7b:<quant> "Motor starts 5 seconds after the start button; stop and E-stop drop it."
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#
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# Requires Ollama 0.3 or newer.
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# num_ctx 8192 matches the sequence length this model was fine-tuned at.
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# Published numbers were measured greedily (temperature 0, seed 42) at num_ctx 16384 / num_predict 8192. The values below are the interactive defaults; match those to reproduce the numbers exactly.
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#
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FROM ./qwen25_coder_7b-tc.q8_0.gguf
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TEMPLATE """{{- if .System }}<|im_start|>system
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{{ .System }}<|im_end|>
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{{ end }}
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{{- range .Messages }}
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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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{{ .Content }}<|im_end|>
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{{ end }}
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{{- end }}<|im_start|>assistant
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"""
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PARAMETER stop "<|im_end|>"
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PARAMETER stop "<|endoftext|>"
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PARAMETER stop "<|im_start|>"
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PARAMETER temperature 0.2
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PARAMETER top_p 0.95
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PARAMETER top_k 20
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# 2) ollama create stcoder-qwen25-7b:<quant> -f Modelfile e.g. ollama create stcoder-qwen25-7b:q8_0 -f Modelfile
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# 3) ollama run stcoder-qwen25-7b:<quant> "Motor starts 5 seconds after the start button; stop and E-stop drop it."
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#
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# Requires Ollama 0.3 or newer; verified on 0.32.5.
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#
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# No TEMPLATE line here, deliberately: Ollama uses the chat template stored inside the GGUF
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# - the template of the tokenizer this model was trained with, so it cannot drift out of
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# sync with the weights. We measured this: adding a TEMPLATE to this file does not change
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# the prompt the model receives (identical prompt token counts with, without, and with a
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# deliberately wrong template). With llama.cpp directly, pass --jinja so llama-cli and
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# llama-server use that same embedded template.
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#
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# num_ctx 8192 matches the sequence length this model was fine-tuned at.
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# Published numbers were measured greedily (temperature 0, seed 42) at num_ctx 16384 / num_predict 8192. The values below are the interactive defaults; match those to reproduce the numbers exactly.
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# Does not support Ollama's thinking switch - passing `think` to /api/chat returns HTTP 400 (measured). It answers with code directly, which is the point of this build.
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FROM ./qwen25_coder_7b-tc.q8_0.gguf
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PARAMETER temperature 0.2
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PARAMETER top_p 0.95
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PARAMETER top_k 20
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