Instructions to use Levelfive/qwen2.5-coder-7b-instruct-q4km-complete with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Levelfive/qwen2.5-coder-7b-instruct-q4km-complete with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Levelfive/qwen2.5-coder-7b-instruct-q4km-complete",
	filename="qwen2.5-coder-7b-instruct-q4_k_m.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Levelfive/qwen2.5-coder-7b-instruct-q4km-complete with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Levelfive/qwen2.5-coder-7b-instruct-q4km-complete:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Levelfive/qwen2.5-coder-7b-instruct-q4km-complete:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Levelfive/qwen2.5-coder-7b-instruct-q4km-complete:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Levelfive/qwen2.5-coder-7b-instruct-q4km-complete: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 Levelfive/qwen2.5-coder-7b-instruct-q4km-complete:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Levelfive/qwen2.5-coder-7b-instruct-q4km-complete: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 Levelfive/qwen2.5-coder-7b-instruct-q4km-complete:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Levelfive/qwen2.5-coder-7b-instruct-q4km-complete:Q4_K_M

Use Docker

docker model run hf.co/Levelfive/qwen2.5-coder-7b-instruct-q4km-complete:Q4_K_M

LM Studio
Jan

vLLM

How to use Levelfive/qwen2.5-coder-7b-instruct-q4km-complete with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Levelfive/qwen2.5-coder-7b-instruct-q4km-complete"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Levelfive/qwen2.5-coder-7b-instruct-q4km-complete",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Levelfive/qwen2.5-coder-7b-instruct-q4km-complete:Q4_K_M

Ollama
How to use Levelfive/qwen2.5-coder-7b-instruct-q4km-complete with Ollama:
```
ollama run hf.co/Levelfive/qwen2.5-coder-7b-instruct-q4km-complete:Q4_K_M
```

Unsloth Studio

How to use Levelfive/qwen2.5-coder-7b-instruct-q4km-complete 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 Levelfive/qwen2.5-coder-7b-instruct-q4km-complete 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 Levelfive/qwen2.5-coder-7b-instruct-q4km-complete to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Levelfive/qwen2.5-coder-7b-instruct-q4km-complete to start chatting

How to use Levelfive/qwen2.5-coder-7b-instruct-q4km-complete with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Levelfive/qwen2.5-coder-7b-instruct-q4km-complete: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": "Levelfive/qwen2.5-coder-7b-instruct-q4km-complete:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Levelfive/qwen2.5-coder-7b-instruct-q4km-complete with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Levelfive/qwen2.5-coder-7b-instruct-q4km-complete: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 Levelfive/qwen2.5-coder-7b-instruct-q4km-complete:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use Levelfive/qwen2.5-coder-7b-instruct-q4km-complete with Docker Model Runner:
```
docker model run hf.co/Levelfive/qwen2.5-coder-7b-instruct-q4km-complete:Q4_K_M
```

Lemonade

How to use Levelfive/qwen2.5-coder-7b-instruct-q4km-complete with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Levelfive/qwen2.5-coder-7b-instruct-q4km-complete:Q4_K_M

Run and chat with the model

lemonade run user.qwen2.5-coder-7b-instruct-q4km-complete-Q4_K_M

List all available models

lemonade list

Levelfive commited on Dec 16, 2025

Commit

75d35a1

verified ·

1 Parent(s): 795c526

Add Q4_KM GGUF, config, and tokenizer files

Browse files

Files changed (3) hide show

.gitattributes +3 -34
README.md +16 -0
qwen2.5-coder-7b-instruct-q4_k_m.gguf +3 -0

.gitattributes CHANGED Viewed

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+*.gguf filter=lfs diff=lfs merge=lfs -text
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 *.model filter=lfs diff=lfs merge=lfs -text
+*.py filter=lfs diff=lfs merge=lfs -text

README.md ADDED Viewed

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+---
+tags:
+- qwen
+- qwen2_5
+- gguf
+- text-generation
+- quantization
+model_name: Qwen2.5 Coder 7B Instruct - Q4_KM (Consolidated)
+---
+# Qwen2.5 Coder 7B Instruct – Q4_KM (Consolidated)
+Source: Qwen/Qwen2.5-Coder-7B-Instruct-GGUF
+This repo bundles the Q4_KM GGUF variant along with necessary configuration and tokenizer files.
+No weight merging. Compatible with llama.cpp ecosystem.

qwen2.5-coder-7b-instruct-q4_k_m.gguf ADDED Viewed

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