Instructions to use bmaxin/8.1-python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bmaxin/8.1-python with Transformers:

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

pipe = pipeline("text-generation", model="bmaxin/8.1-python")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bmaxin/8.1-python")
model = AutoModelForCausalLM.from_pretrained("bmaxin/8.1-python")
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]:]))

llama-cpp-python

How to use bmaxin/8.1-python with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="bmaxin/8.1-python",
	filename="unsloth.Q4_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 bmaxin/8.1-python with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf bmaxin/8.1-python:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf bmaxin/8.1-python:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf bmaxin/8.1-python:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf bmaxin/8.1-python: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 bmaxin/8.1-python:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf bmaxin/8.1-python: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 bmaxin/8.1-python:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bmaxin/8.1-python:Q4_K_M

Use Docker

docker model run hf.co/bmaxin/8.1-python:Q4_K_M

LM Studio
Jan

vLLM

How to use bmaxin/8.1-python with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bmaxin/8.1-python"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bmaxin/8.1-python",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bmaxin/8.1-python:Q4_K_M

SGLang

How to use bmaxin/8.1-python 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 "bmaxin/8.1-python" \
    --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": "bmaxin/8.1-python",
		"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 "bmaxin/8.1-python" \
        --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": "bmaxin/8.1-python",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use bmaxin/8.1-python with Ollama:
```
ollama run hf.co/bmaxin/8.1-python:Q4_K_M
```

Unsloth Studio new

How to use bmaxin/8.1-python 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 bmaxin/8.1-python 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 bmaxin/8.1-python to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for bmaxin/8.1-python to start chatting

Pi new

How to use bmaxin/8.1-python with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf bmaxin/8.1-python: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": "bmaxin/8.1-python:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use bmaxin/8.1-python with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf bmaxin/8.1-python: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 bmaxin/8.1-python:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use bmaxin/8.1-python with Docker Model Runner:
```
docker model run hf.co/bmaxin/8.1-python:Q4_K_M
```

Lemonade

How to use bmaxin/8.1-python with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bmaxin/8.1-python:Q4_K_M

Run and chat with the model

lemonade run user.8.1-python-Q4_K_M

List all available models

lemonade list

8.1-python / README.md

bmaxin

Create README.md

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	---
	license: llama3.1
	language:
	- en
	- py
	library_name: transformers
	tags:
	- llama-3.1
	- python
	- code-generation
	- instruction-following
	- fine-tune
	- alpaca
	- unsloth
	base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
	datasets:
	- iamtarun/python_code_instructions_18k_alpaca
	---
	---

	# Llama-3.1-8B-Instruct-Python-Alpaca-Unsloth

	This is a fine-tuned version of Meta's `Llama-3.1-8B-Instruct` model, specialized for Python code generation. It was trained on the high-quality `iamtarun/python_code_instructions_18k_alpaca` dataset using the Unsloth library for significantly faster training and reduced memory usage.

	The result is a powerful and responsive coding assistant, designed to follow instructions and generate accurate, high-quality Python code.

	---
	## ## Model Details 🛠️

	* Base Model: `meta-llama/Meta-Llama-3.1-8B-Instruct`
	* Dataset: `iamtarun/python_code_instructions_18k_alpaca` (18,000 instruction-following examples for Python)
	* Fine-tuning Technique: QLoRA (4-bit Quantization with LoRA adapters)
	* Framework: Unsloth (for up to 2x faster training and optimized memory)

	---
	## ## How to Use 👨‍💻

	This model is designed to be used with the Unsloth library for maximum performance, but it can also be used with the standard Hugging Face `transformers` library. For the best results, always use the Llama 3 chat template.

	### ### Using with Unsloth (Recommended)

	```python
	from unsloth import FastLanguageModel
	import torch

	model, tokenizer = FastLanguageModel.from_pretrained(
	model_name = "YOUR_USERNAME/YOUR_MODEL_NAME", # REMEMBER TO REPLACE THIS
	max_seq_length = 4096,
	dtype = None,
	load_in_4bit = True,
	)

	# Prepare the model for faster inference
	FastLanguageModel.for_inference(model)

	messages = [
	{
	"role": "system",
	"content": "You are a helpful Python coding assistant. Please provide a clear, concise, and correct Python code response to the user's request."
	},
	{
	"role": "user",
	"content": "Create a Python function that finds the nth Fibonacci number using recursion."
	},
	]

	input_ids = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	return_tensors="pt"
	).to(model.device)

	outputs = model.generate(
	input_ids,
	max_new_tokens=200,
	do_sample=True,
	temperature=0.6,
	top_p=0.9,
	eos_token_id=tokenizer.eos_token_id
	)

	response = outputs[0][input_ids.shape[-1]:]
	print(tokenizer.decode(response, skip_special_tokens=True))