Instructions to use Dzluck/GRM-Coder-14b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Dzluck/GRM-Coder-14b-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Dzluck/GRM-Coder-14b-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Dzluck/GRM-Coder-14b-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Dzluck/GRM-Coder-14b-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 Dzluck/GRM-Coder-14b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Dzluck/GRM-Coder-14b-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 Dzluck/GRM-Coder-14b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Dzluck/GRM-Coder-14b-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 Dzluck/GRM-Coder-14b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Dzluck/GRM-Coder-14b-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 Dzluck/GRM-Coder-14b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Dzluck/GRM-Coder-14b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Dzluck/GRM-Coder-14b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Dzluck/GRM-Coder-14b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Dzluck/GRM-Coder-14b-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": "Dzluck/GRM-Coder-14b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Dzluck/GRM-Coder-14b-GGUF:Q4_K_M

SGLang

How to use Dzluck/GRM-Coder-14b-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Dzluck/GRM-Coder-14b-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Dzluck/GRM-Coder-14b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Dzluck/GRM-Coder-14b-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Dzluck/GRM-Coder-14b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Dzluck/GRM-Coder-14b-GGUF with Ollama:
```
ollama run hf.co/Dzluck/GRM-Coder-14b-GGUF:Q4_K_M
```

Unsloth Studio

How to use Dzluck/GRM-Coder-14b-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 Dzluck/GRM-Coder-14b-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 Dzluck/GRM-Coder-14b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Dzluck/GRM-Coder-14b-GGUF to start chatting

How to use Dzluck/GRM-Coder-14b-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Dzluck/GRM-Coder-14b-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": "Dzluck/GRM-Coder-14b-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use Dzluck/GRM-Coder-14b-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Dzluck/GRM-Coder-14b-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 "Dzluck/GRM-Coder-14b-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"

Docker Model Runner
How to use Dzluck/GRM-Coder-14b-GGUF with Docker Model Runner:
```
docker model run hf.co/Dzluck/GRM-Coder-14b-GGUF:Q4_K_M
```

Lemonade

How to use Dzluck/GRM-Coder-14b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Dzluck/GRM-Coder-14b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.GRM-Coder-14b-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Dzluck/GRM-Coder-14b-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 Dzluck/GRM-Coder-14b-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 Dzluck/GRM-Coder-14b-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

GRM-Coder-14b-GGUF

File size: 3,177 Bytes

03e960e

# OrionLLM/GRM-Coder-14b — Ollama Modelfile (fixed template, no unsupported funcs)
# ollama create GRM-Coder-14b:Q5 -f GRM-Coder-14b.modelfile

FROM ./GRM-Coder-14b-Q5_K_M.gguf

SYSTEM """You are GRM-Coder, an expert competitive programming assistant based on Qwen3-14B. You excel at algorithm design, data structures, complexity analysis, and writing correct, efficient code for contest problems (Codeforces, AtCoder, LeetCode Hard, ICPC-style).

Guidelines:
- Clarify constraints, edge cases, and I/O format when ambiguous.
- Prefer clear reasoning: problem restatement → approach → complexity → full solution code.
- Default to Python 3 unless the user specifies another language.
- Write complete, runnable solutions with correct handling of edge cases.
- When multiple approaches exist, briefly compare them and pick the best fit for the constraints.
"""

# Qwen3 chat + thinking — only uses Ollama-supported template funcs
# (eq/ne/and/or/not/index/len/slice/range — NO sub/add/mul/div)
TEMPLATE """{{- $lastUserIdx := -1 -}}
{{- range $idx, $msg := .Messages -}}
{{- if eq $msg.Role "user" }}{{ $lastUserIdx = $idx }}{{ end -}}
{{- end -}}
{{- $lastRole := "" -}}
{{- range .Messages }}{{ $lastRole = .Role }}{{ end -}}
{{- if or .System .Tools }}<|im_start|>system
{{- if .System }}
{{ .System }}
{{- end }}
{{- if .Tools }}

# Tools

You may call one or more functions to assist with the user query.

You are provided with function signatures within <tools></tools> XML tags:
<tools>
{{- range .Tools }}
{"type": "function", "function": {{ .Function }}}
{{- end }}
</tools>

For each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
<tool_call>
{"name": <function-name>, "arguments": <args-json-object>}
</tool_call>
{{- end }}<|im_end|>
{{ end }}
{{- range $i, $_ := .Messages }}
{{- $last := eq (len (slice $.Messages $i)) 1 }}
{{- if eq .Role "user" }}<|im_start|>user
{{ .Content }}<|im_end|>
{{ else if eq .Role "assistant" }}<|im_start|>assistant
{{- if .Thinking }}
{{ .Thinking }}
{{- end }}
{{- if .Content }}
{{ .Content }}
{{- end }}
{{- if .ToolCalls }}
{{- range .ToolCalls }}
<tool_call>
{"name": "{{ .Function.Name }}", "arguments": {{ .Function.Arguments }}}
</tool_call>
{{- end }}
{{- end }}<|im_end|>
{{ else if eq .Role "tool" }}<|im_start|>user
<tool_response>
{{ .Content }}
</tool_response><|im_end|>
{{ end }}
{{- end }}
{{- if ne $lastRole "assistant" }}
{{- if and .IsThinkSet (not .Think) }}<|im_start|>assistant
<think>

</think>

{{ else }}<|im_start|>assistant
{{ end }}
{{- else if not .Messages }}
{{- if and .IsThinkSet (not .Think) }}{{ .Prompt }}<|im_start|>assistant
<think>

</think>

{{ else }}{{ .Prompt }}<|im_start|>assistant
{{ end }}
{{- end }}"""

PARAMETER temperature 0.6
PARAMETER top_k 20
PARAMETER top_p 0.95
PARAMETER min_p 0.0
PARAMETER repeat_penalty 1.05
PARAMETER num_ctx 32768
PARAMETER num_predict 8192

PARAMETER stop "<|im_start|>"
PARAMETER stop "<|im_end|>"
PARAMETER stop "<|endoftext|>"

LICENSE """
Apache License 2.0
Model: Dzluck/GRM-Coder-14b-GGUF
Base model: Qwen/Qwen3-14B
https://huggingface.co/Dzluck/GRM-Coder-14b-GGUF
"""