Instructions to use KeefeBuild/Keefe-Discere with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KeefeBuild/Keefe-Discere with Transformers:

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

pipe = pipeline("text-generation", model="KeefeBuild/Keefe-Discere")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KeefeBuild/Keefe-Discere")
model = AutoModelForCausalLM.from_pretrained("KeefeBuild/Keefe-Discere", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

PEFT
How to use KeefeBuild/Keefe-Discere with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use KeefeBuild/Keefe-Discere 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 KeefeBuild/Keefe-Discere:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf KeefeBuild/Keefe-Discere:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf KeefeBuild/Keefe-Discere:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf KeefeBuild/Keefe-Discere: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 KeefeBuild/Keefe-Discere:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf KeefeBuild/Keefe-Discere: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 KeefeBuild/Keefe-Discere:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf KeefeBuild/Keefe-Discere:Q4_K_M

Use Docker

docker model run hf.co/KeefeBuild/Keefe-Discere:Q4_K_M

LM Studio
Jan

vLLM

How to use KeefeBuild/Keefe-Discere with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/KeefeBuild/Keefe-Discere:Q4_K_M

SGLang

How to use KeefeBuild/Keefe-Discere 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 "KeefeBuild/Keefe-Discere" \
    --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": "KeefeBuild/Keefe-Discere",
		"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 "KeefeBuild/Keefe-Discere" \
        --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": "KeefeBuild/Keefe-Discere",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use KeefeBuild/Keefe-Discere with Ollama:
```
ollama run hf.co/KeefeBuild/Keefe-Discere:Q4_K_M
```

Unsloth Studio

How to use KeefeBuild/Keefe-Discere 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 KeefeBuild/Keefe-Discere 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 KeefeBuild/Keefe-Discere to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for KeefeBuild/Keefe-Discere to start chatting

How to use KeefeBuild/Keefe-Discere with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf KeefeBuild/Keefe-Discere:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "KeefeBuild/Keefe-Discere:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use KeefeBuild/Keefe-Discere with Docker Model Runner:
```
docker model run hf.co/KeefeBuild/Keefe-Discere:Q4_K_M
```

Lemonade

How to use KeefeBuild/Keefe-Discere with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull KeefeBuild/Keefe-Discere:Q4_K_M

Run and chat with the model

lemonade run user.Keefe-Discere-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use KeefeBuild/Keefe-Discere with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf KeefeBuild/Keefe-Discere: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 KeefeBuild/Keefe-Discere:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use KeefeBuild/Keefe-Discere with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf KeefeBuild/Keefe-Discere: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 "KeefeBuild/Keefe-Discere: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"

Keefe-Discere

An 8B-class instruction-following language model focused on reasoning, coding, mathematics, and agentic tool use.

Quick Start • Model Info • Training Data • Roadmap • Citation

Overview

Keefe-Discere is an independently developed language-model project by KeefeBuild, built on the Qwen2.5-7B instruction-tuned architecture and enhanced through a two-stage process:

Model Merging (DARE-TIES): Combining general, coding, and mathematics specialists into a single balanced 15GB checkpoint.
Targeted Post-Training (QLoRA v1.1): Fine-tuning a LoRA adapter on curated instruction, reasoning, and code-execution data to improve agentic tool use and mathematical reliability.

The project is designed as a general-purpose, locally-deployable language model with an emphasis on:

🧠 Reasoning and structured problem solving
🔢 Mathematics and quantitative tasks
💻 Programming, debugging, and code execution
🛠️ Agentic tool use (Python execution, function calling)
💬 General instruction following
🏠 Private, self-hosted, and offline inference

Important: Keefe-Discere is an independent model project and is not an official Qwen model.

Quick Start

🤗 Transformers (Python)

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

# 1. Load the merged base model
base_model_id = "KeefeBuild/Keefe-Discere"
tokenizer = AutoTokenizer.from_pretrained(base_model_id)
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id, torch_dtype=torch.bfloat16, device_map="auto"
)

# 2. Attach the v1.1 LoRA adapter for enhanced coding/tool use
model = PeftModel.from_pretrained(base_model, base_model_id)

messages = [
    {"role": "system", "content": "You are Keefe-Discere. Write clean Python code and use print() to output final answers."},
    {"role": "user", "content": "Calculate the sum of the first 15 prime numbers."}
]

inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(inputs, max_new_tokens=512, temperature=0.1)
print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for KeefeBuild/Keefe-Discere

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Adapter

(2645)

this model

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Keefe-Discere

Keefe-Discere

Overview

Quick Start

🤗 Transformers (Python)

Model tree for KeefeBuild/Keefe-Discere

Datasets used to train KeefeBuild/Keefe-Discere

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