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"

Mikrodev commited on 23 days ago

Commit

0f50dd6

verified ·

1 Parent(s): 460e268

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README.md CHANGED Viewed

@@ -58,10 +58,8 @@ stcoder-qwen25-7b is a chat-only model fine-tuned from Qwen2.5-Coder-7B-Instruct
 | Build | File | Size | Free VRAM needed | In repo | Verdict |
 |---|---|---|---|---|---|
 | **Q8_0** | `qwen25_coder_7b-tc.q8_0.gguf` | 7.54 GiB | 9.5 GiB | in this repo | **recommended** — build used in the study below |
-| **Q6_K** | `qwen25_coder_7b-tc.q6_k.gguf` | 5.82 GiB | 7.8 GiB | being uploaded | fine |
-| **Q4_K_M** | `qwen25_coder_7b-tc.q4_k_m.gguf` | 4.36 GiB | 6.4 GiB | being uploaded | not recommended |
-> **Still uploading:** Q6_K, Q4_K_M. If a download 404s that build has not landed yet — check the repo's *Files* tab, or take one marked *in this repo*.
 *Free VRAM* is the file plus roughly 2 GiB for the 8192-token context and runtime. Less than that
 and the runtime spills layers to system RAM: it still answers, but the speed figures below no
@@ -74,7 +72,7 @@ slower generation.
 | Artefact | What it is | Size | In repo |
 |---|---|---|---|
-| `lora_adapter/` | The LoRA adapter this model was trained as — PEFT adapter on the base model above. Merge it yourself, stack it, or continue training from it. | small | being uploaded |
 | f16 merged weights | f16 is the merged fine-tune at full precision: the reference build, for evaluation or for producing your own quantisations. | ~14.20 GiB | not shipped - rebuild from the adapter (snippet below) |
 | `Modelfile` | One ready-to-run Ollama definition: the system prompt, the sampling defaults and `num_ctx 8192` (the sequence length this model was trained at). It deliberately carries **no chat template** — Ollama uses the chat template stored inside the GGUF, so the chat format is the trained one and cannot drift out of sync with this file. Its `FROM` points at the Q8_0 file; edit that one line if you downloaded a different quant. | small | in this repo |
 | `article_stcoder-qwen25-7b.html`, `article_stcoder-qwen25-7b.docx` | The full evaluation report for this model. | |

 | Build | File | Size | Free VRAM needed | In repo | Verdict |
 |---|---|---|---|---|---|
 | **Q8_0** | `qwen25_coder_7b-tc.q8_0.gguf` | 7.54 GiB | 9.5 GiB | in this repo | **recommended** — build used in the study below |
+| **Q6_K** | `qwen25_coder_7b-tc.q6_k.gguf` | 5.82 GiB | 7.8 GiB | in this repo | fine |
+| **Q4_K_M** | `qwen25_coder_7b-tc.q4_k_m.gguf` | 4.36 GiB | 6.4 GiB | in this repo | not recommended |
 *Free VRAM* is the file plus roughly 2 GiB for the 8192-token context and runtime. Less than that
 and the runtime spills layers to system RAM: it still answers, but the speed figures below no
 | Artefact | What it is | Size | In repo |
 |---|---|---|---|
+| `lora_adapter/` | The LoRA adapter this model was trained as — PEFT adapter on the base model above. Merge it yourself, stack it, or continue training from it. | small | in this repo |
 | f16 merged weights | f16 is the merged fine-tune at full precision: the reference build, for evaluation or for producing your own quantisations. | ~14.20 GiB | not shipped - rebuild from the adapter (snippet below) |
 | `Modelfile` | One ready-to-run Ollama definition: the system prompt, the sampling defaults and `num_ctx 8192` (the sequence length this model was trained at). It deliberately carries **no chat template** — Ollama uses the chat template stored inside the GGUF, so the chat format is the trained one and cannot drift out of sync with this file. Its `FROM` points at the Q8_0 file; edit that one line if you downloaded a different quant. | small | in this repo |
 | `article_stcoder-qwen25-7b.html`, `article_stcoder-qwen25-7b.docx` | The full evaluation report for this model. | |