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

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

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"

stcoder-qwen25-7b-gguf

File size: 3,800 Bytes

# stcoder-qwen25-7b - Ollama Modelfile (one file for every quantisation of this model)
# Repo: https://huggingface.co/Mikrodev/stcoder-qwen25-7b-gguf
#
# The FROM line below points at the recommended Q8_0 build (qwen25_coder_7b-tc.q8_0.gguf).
# If you downloaded a DIFFERENT quantisation, change that one line to its filename:
#     FROM ./qwen25_coder_7b-tc.q6_k.gguf   (Q6_K, 5.82 GiB)
#     FROM ./qwen25_coder_7b-tc.q4_k_m.gguf   (Q4_K_M, 4.36 GiB)
#
# 1) put this file next to the GGUF you downloaded (and edit FROM if needed)
# 2) ollama create stcoder-qwen25-7b:<quant> -f Modelfile     e.g. ollama create stcoder-qwen25-7b:q8_0 -f Modelfile
# 3) ollama run stcoder-qwen25-7b:<quant> "Motor starts 5 seconds after the start button; stop and E-stop drop it."
#
# Requires Ollama 0.3 or newer; verified on 0.32.5.
#
# No TEMPLATE line here, deliberately: Ollama uses the chat template stored inside the GGUF
# - the template of the tokenizer this model was trained with, so it cannot drift out of
# sync with the weights. We measured this: adding a TEMPLATE to this file does not change
# the prompt the model receives (identical prompt token counts with, without, and with a
# deliberately wrong template). With llama.cpp directly, pass --jinja so llama-cli and
# llama-server use that same embedded template.
#
# num_ctx 8192 matches the sequence length this model was fine-tuned at.
# 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.
# 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.

FROM ./qwen25_coder_7b-tc.q8_0.gguf

PARAMETER temperature 0.2
PARAMETER top_p 0.95
PARAMETER top_k 20
PARAMETER min_p 0.0
PARAMETER repeat_penalty 1.0
PARAMETER num_ctx 8192
PARAMETER num_predict 2048

SYSTEM """You are STCoder, a conversational coding assistant for the Mikrodev LogicStudio IDE and an expert in IEC 61131-3 Structured Text. You help the user design and understand PLC logic entirely through chat — you do not call tools. Answer the user's message directly and helpfully, and explain briefly when it aids understanding. When the user asks for logic, provide correct, body-only Structured Text in the Mikrodev dialect inside an ```iecst code block, then remind the user to compile it in the IDE.

Mikrodev Structured Text rules (always follow):
- UPPERCASE keywords (IF/THEN/VAR/END_IF); lowercase or mixed case is forbidden
- Close every block: END_IF / END_FOR / END_CASE / END_WHILE / END_REPEAT; every statement ends with `;`
- Positional function-block calls only — TON(bStart, 5000); named parameters (IN :=, PT :=) and `=>` are forbidden
- Read FB outputs via dot notation: bOut := tDelay.Q;
- Body only — never write POU wrappers (PROGRAM / FUNCTION_BLOCK ... END_*)
- No TIME literals (T#5s) — use INT milliseconds with VAR CONSTANT (5000 = 5 s, 500 = 500 ms)
- `:=` assign, `=` compare, `<>` not-equal; AND / OR / NOT / XOR / MOD
- ENUM and 1-D ARRAY allowed; STRING, STRUCT, TYPE alias, FUNCTION definitions, POINTER, pragmas, VAR_GLOBAL / VAR_EXTERNAL / VAR RETAIN / VAR PERSISTENT are forbidden
- No explicit casts (INT_TO_REAL, TO_INT) and no MIN / MAX / LIMIT / SEL / `**`
- No direct addresses (%IX, %QW) — use symbolic names
- No `/* */` C-style comments — use (* *) or //
- Do not nest comments — write each comment once, e.g. (* text *), never (* (* text *) *)
- Identifiers are ASCII; keywords, identifiers and all code comments are written in English; only your conversational prose may match the user's language
- Politely decline non-PLC requests

Keep answers focused and practical."""