Instructions to use Nerdsking/nerdsking-python-coder-3B-i with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nerdsking/nerdsking-python-coder-3B-i with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Nerdsking/nerdsking-python-coder-3B-i",
	filename="nerdsking-python-coder-3B-i_Q5_k_m.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Nerdsking/nerdsking-python-coder-3B-i with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Nerdsking/nerdsking-python-coder-3B-i:Q5_K_M
# Run inference directly in the terminal:
llama-cli -hf Nerdsking/nerdsking-python-coder-3B-i:Q5_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Nerdsking/nerdsking-python-coder-3B-i:Q5_K_M
# Run inference directly in the terminal:
llama-cli -hf Nerdsking/nerdsking-python-coder-3B-i:Q5_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 Nerdsking/nerdsking-python-coder-3B-i:Q5_K_M
# Run inference directly in the terminal:
./llama-cli -hf Nerdsking/nerdsking-python-coder-3B-i:Q5_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 Nerdsking/nerdsking-python-coder-3B-i:Q5_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Nerdsking/nerdsking-python-coder-3B-i:Q5_K_M

Use Docker

docker model run hf.co/Nerdsking/nerdsking-python-coder-3B-i:Q5_K_M

LM Studio
Jan

vLLM

How to use Nerdsking/nerdsking-python-coder-3B-i with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nerdsking/nerdsking-python-coder-3B-i"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nerdsking/nerdsking-python-coder-3B-i",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Nerdsking/nerdsking-python-coder-3B-i:Q5_K_M

Ollama
How to use Nerdsking/nerdsking-python-coder-3B-i with Ollama:
```
ollama run hf.co/Nerdsking/nerdsking-python-coder-3B-i:Q5_K_M
```

Unsloth Studio new

How to use Nerdsking/nerdsking-python-coder-3B-i 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 Nerdsking/nerdsking-python-coder-3B-i 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 Nerdsking/nerdsking-python-coder-3B-i to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Nerdsking/nerdsking-python-coder-3B-i to start chatting

Pi new

How to use Nerdsking/nerdsking-python-coder-3B-i with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Nerdsking/nerdsking-python-coder-3B-i:Q5_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": "Nerdsking/nerdsking-python-coder-3B-i:Q5_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Nerdsking/nerdsking-python-coder-3B-i with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Nerdsking/nerdsking-python-coder-3B-i:Q5_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 Nerdsking/nerdsking-python-coder-3B-i:Q5_K_M

Run Hermes

hermes

Docker Model Runner
How to use Nerdsking/nerdsking-python-coder-3B-i with Docker Model Runner:
```
docker model run hf.co/Nerdsking/nerdsking-python-coder-3B-i:Q5_K_M
```

Lemonade

How to use Nerdsking/nerdsking-python-coder-3B-i with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Nerdsking/nerdsking-python-coder-3B-i:Q5_K_M

Run and chat with the model

lemonade run user.nerdsking-python-coder-3B-i-Q5_K_M

List all available models

lemonade list

Nerdsking commited on Dec 22, 2025

Commit

ff07f06

verified ·

1 Parent(s): 6fee672

Update README.md

Browse files

1. Overview

Nerdsking-python-coder-3B-i is a 3B parameter partially uncensored model focused in Python, with English as main language. It was massively trained in python, therefore despite the fact it can code in other languages as well, the performance will be not in the same level as the one achieved while using python.

Key Characteristics:

✔ Parameter count: 3B
✔ Primary domain: Python programming
✔ Secondary capabilities: General coding, technical English
✔ Training focus: Python logic, standard library usage, algorithmic reasoning
✔ Alignment: Partially uncensored (developer-oriented)

2. Benchmark
After months of refining, Nerdsking-python-coder-3B-i has achieved 88.41 in HumanEval (bf16), placing it among the highest-performing Python-focused 3B models reported on HumanEval. Surpassing much bigger models in that area.

2.1 specs:

✔ Official HumanEval execution protocol
✔ Deterministic, zero-shot evaluation
✔ temperature = 0.1
✔ do_sample = False
✔ zero-shot, pass@1
✔ dtype == "bfloat16"
✔ Fixed system prompt: “You are an expert Python coding assistant.”
✔ Evaluated on fully merged weights

3. S.o.n.n.
The model was treated under "s.o.n.n." (single omni neural network), a concept created by IPMN at Nerdsking.com that is both a precise way of fine tunning/altering existing models, as well the basis for a new Artificial Intelligence standard, currently in development.

While used in pre-existing models, s.o.n.n. allows:
• Parameter-preserving refinement methodology
• Focus on global behavioral shaping, not task-local adapters
• Avoid fragmentation common in multi-adapter or task-siloed approaches

4. Intended Use & Limitations

Intended use:

✔Python development
✔Algorithmic problem solving
✔Code reasoning and refactoring
✔Developer-centric workflows

Known limitations:

• Not optimized for non-Python languages
• Not instruction-chat aligned for conversational safety
• Not trained for legal, medical, or policy compliance use cases

5. Quick Start (Inference)

<code>
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Nerdsking/Nerdsking-python-coder-3B-i"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="bfloat16",
device_map="auto"
)

prompt = "Write a Python function that checks if a number is prime."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=200)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
</code>

6. Ethical & Safety Notes

This model is intended for technical and research use. Due to relaxed alignment constraints, outputs should be reviewed before deployment in production or public-facing systems.

Files changed (1) hide show

README.md +14 -3

README.md CHANGED Viewed

@@ -1,3 +1,14 @@
----
-license: fair-noncommercial-research-license
----

+---
+license: fair-noncommercial-research-license
+language:
+- en
+- pt
+metrics:
+      - type: { HumanEval zero-shot pass@1}         # Required. Example: wer. Use metric id from https://hf.co/metrics
+        value: {88.41}       # Required. Example: 20.90
+base_model:
+- Qwen/Qwen2.5-Coder-3B-Instruct
+pipeline_tag: text-generation
+tags:
+- code
+---