Instructions to use Friehub/fwen-14b-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Friehub/fwen-14b-v1 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Friehub/fwen-14b-v1",
	filename="fwen-14b-q4_k_m-v1.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 Friehub/fwen-14b-v1 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Friehub/fwen-14b-v1:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Friehub/fwen-14b-v1:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Friehub/fwen-14b-v1:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Friehub/fwen-14b-v1: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 Friehub/fwen-14b-v1:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Friehub/fwen-14b-v1: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 Friehub/fwen-14b-v1:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Friehub/fwen-14b-v1:Q4_K_M

Use Docker

docker model run hf.co/Friehub/fwen-14b-v1:Q4_K_M

LM Studio
Jan

vLLM

How to use Friehub/fwen-14b-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Friehub/fwen-14b-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Friehub/fwen-14b-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Friehub/fwen-14b-v1:Q4_K_M

Ollama
How to use Friehub/fwen-14b-v1 with Ollama:
```
ollama run hf.co/Friehub/fwen-14b-v1:Q4_K_M
```

Unsloth Studio

How to use Friehub/fwen-14b-v1 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 Friehub/fwen-14b-v1 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 Friehub/fwen-14b-v1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Friehub/fwen-14b-v1 to start chatting

How to use Friehub/fwen-14b-v1 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Friehub/fwen-14b-v1: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": "Friehub/fwen-14b-v1:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Friehub/fwen-14b-v1 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Friehub/fwen-14b-v1: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 Friehub/fwen-14b-v1:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use Friehub/fwen-14b-v1 with Docker Model Runner:
```
docker model run hf.co/Friehub/fwen-14b-v1:Q4_K_M
```

Lemonade

How to use Friehub/fwen-14b-v1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Friehub/fwen-14b-v1:Q4_K_M

Run and chat with the model

lemonade run user.fwen-14b-v1-Q4_K_M

List all available models

lemonade list

Fwen-14B-v1

Friehub + Qwen — a fine-tuned 14B software engineering and CS tutor.

Trained on 7,106 instruction pairs extracted from 76 textbooks spanning algorithms, system design, networking, databases, compilers, and programming languages (Python, Go, Rust, JavaScript, C++). Answers are grounded in real engineering source material, not internet-scale guesswork.

Model Details

Property	Value
Base Model	Qwen2.5-14B-Instruct
Fine-tuning	QLoRA (4-bit NF4), LoRA rank 8, alpha 16, dropout 0.05
Training Data	7,106 high-quality QA pairs (balanced, deduplicated, task-validated)
Task Types	15 modes: code explanation, debugging, review, generation, complexity analysis, testing, modernization, full implementation, code completion, production scenarios, cross-source synthesis, diagram generation, prose, math, quizzes
Data Mix	40% code, 20% debug, 25% design, 15% docs/Q&A
Epochs	2
Effective Batch Size	32 (micro-batch 1 × 32 accum)
Learning Rate	1e-4
Warmup	5%
GPU	A100-40GB
Training Time	164 min
Language Coverage	Python, Go, Rust, JavaScript, TypeScript, Java, C, C++, SQL

Evaluation (50 held-out QA pairs)

Metric	Value
Average QA Score	3.3/5
Hallucination Rate	~2%
Strongest Domains	Algorithms, Databases, Linux, Web APIs
Weakest Domains	Interview prep, abstract design principles

Scored by human audit against ground-truth answers generated from the same source textbooks.

Capabilities

Explain CS concepts from 70+ textbooks with source attribution
Write production-grade code in Python, Go, Rust, JS, TS, Java, C with modern syntax (2024 editions)
Debug and review code — find subtle bugs, suggest improvements with rationale
Analyze algorithm complexity — trace loops, recursion, data structures
Synthesize across books — when sources disagree, attribute each position
Generate Mermaid diagrams — architecture, sequences, class hierarchies
Multi-turn tutoring — conversational teaching with follow-ups

Files

File	Size	Use
`fwen-14b-q4_k_m-v1.gguf`	8 GB	Production serving (Q4_K_M quantization)
`fwen-14b-q8_0-v1.gguf`	14 GB	Benchmark evaluation (Q8_0 quantization)

Usage

Ollama

ollama create fwen:14b -f Modelfile
ollama run fwen:14b

llama.cpp

./llama-cli -m fwen-14b-q4_k_m-v1.gguf -p "Explain Rust ownership" -n 512

HF Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("friehub/fwen-14b-v1")

Limitations

Trained on 7K pairs — smaller than typical fine-tune datasets. Expect adequate but not expert-level depth.
Rust/Go underrepresented in training data (v2 will address this).
Source attribution is sometimes vague ("the text emphasizes" vs "Chapter 4 of Code Simplicity states").

Training Pipeline

PDF ingestion → domain classification → code/math/prose unit extraction (212,616 units)
15 task-type instruction pairs generated by DeepSeek V4 Pro/Flash
Quality filter: length, dedup, relevance (cosine similarity), task validators
Language balance: capped at 25% Python code pairs
QLoRA fine-tune with Unsloth on A100-40GB

Citation

@model{fwen-14b-v1,
  author = {Friehub},
  title = {Fwen-14B: A Fine-Tuned Software Engineering Tutor},
  version = {v1},
  year = 2026,
  url = {https://huggingface.co/friehub/fwen-14b-v1}
}

Downloads last month: 66

GGUF

Model size

15B params

Architecture

qwen2

Hardware compatibility

4-bit

8-bit