Instructions to use tensorblock/Python-Code-33B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tensorblock/Python-Code-33B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="tensorblock/Python-Code-33B-GGUF",
	filename="Python-Code-33B-Q2_K.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use tensorblock/Python-Code-33B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tensorblock/Python-Code-33B-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf tensorblock/Python-Code-33B-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tensorblock/Python-Code-33B-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf tensorblock/Python-Code-33B-GGUF:Q2_K

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 tensorblock/Python-Code-33B-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf tensorblock/Python-Code-33B-GGUF:Q2_K

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 tensorblock/Python-Code-33B-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tensorblock/Python-Code-33B-GGUF:Q2_K

Use Docker

docker model run hf.co/tensorblock/Python-Code-33B-GGUF:Q2_K

LM Studio
Jan
Ollama
How to use tensorblock/Python-Code-33B-GGUF with Ollama:
```
ollama run hf.co/tensorblock/Python-Code-33B-GGUF:Q2_K
```

Unsloth Studio new

How to use tensorblock/Python-Code-33B-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 tensorblock/Python-Code-33B-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 tensorblock/Python-Code-33B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for tensorblock/Python-Code-33B-GGUF to start chatting

Docker Model Runner
How to use tensorblock/Python-Code-33B-GGUF with Docker Model Runner:
```
docker model run hf.co/tensorblock/Python-Code-33B-GGUF:Q2_K
```

Lemonade

How to use tensorblock/Python-Code-33B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tensorblock/Python-Code-33B-GGUF:Q2_K

Run and chat with the model

lemonade run user.Python-Code-33B-GGUF-Q2_K

List all available models

lemonade list

Python-Code-33B-GGUF / README.md

morriszms

Update README.md

ba28e03 verified 11 months ago

8.42 kB

language:
  - en
license: cc-by-nc-nd-4.0
tags:
  - code
  - TensorBlock
  - GGUF
datasets:
  - ajibawa-2023/Python-Code-23k-ShareGPT
base_model: ajibawa-2023/Python-Code-33B
model-index:
  - name: Python-Code-33B
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: AI2 Reasoning Challenge (25-Shot)
          type: ai2_arc
          config: ARC-Challenge
          split: test
          args:
            num_few_shot: 25
        metrics:
          - type: acc_norm
            value: 56.31
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Python-Code-33B
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: HellaSwag (10-Shot)
          type: hellaswag
          split: validation
          args:
            num_few_shot: 10
        metrics:
          - type: acc_norm
            value: 81.01
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Python-Code-33B
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU (5-Shot)
          type: cais/mmlu
          config: all
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 54.22
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Python-Code-33B
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: TruthfulQA (0-shot)
          type: truthful_qa
          config: multiple_choice
          split: validation
          args:
            num_few_shot: 0
        metrics:
          - type: mc2
            value: 44.39
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Python-Code-33B
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: Winogrande (5-shot)
          type: winogrande
          config: winogrande_xl
          split: validation
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 75.22
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Python-Code-33B
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GSM8k (5-shot)
          type: gsm8k
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 19.18
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Python-Code-33B
          name: Open LLM Leaderboard

ajibawa-2023/Python-Code-33B - GGUF

This repo contains GGUF format model files for ajibawa-2023/Python-Code-33B.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4242.

Our projects

Awesome MCP Servers	TensorBlock Studio

A comprehensive collection of Model Context Protocol (MCP) servers.	A lightweight, open, and extensible multi-LLM interaction studio.
👀 See what we built 👀	👀 See what we built 👀

## Prompt template

Model file specification

Filename	Quant type	File Size	Description
Python-Code-33B-Q2_K.gguf	Q2_K	12.049 GB	smallest, significant quality loss - not recommended for most purposes
Python-Code-33B-Q3_K_S.gguf	Q3_K_S	14.064 GB	very small, high quality loss
Python-Code-33B-Q3_K_M.gguf	Q3_K_M	15.776 GB	very small, high quality loss
Python-Code-33B-Q3_K_L.gguf	Q3_K_L	17.280 GB	small, substantial quality loss
Python-Code-33B-Q4_0.gguf	Q4_0	18.356 GB	legacy; small, very high quality loss - prefer using Q3_K_M
Python-Code-33B-Q4_K_S.gguf	Q4_K_S	18.482 GB	small, greater quality loss
Python-Code-33B-Q4_K_M.gguf	Q4_K_M	19.621 GB	medium, balanced quality - recommended
Python-Code-33B-Q5_0.gguf	Q5_0	22.395 GB	legacy; medium, balanced quality - prefer using Q4_K_M
Python-Code-33B-Q5_K_S.gguf	Q5_K_S	22.395 GB	large, low quality loss - recommended
Python-Code-33B-Q5_K_M.gguf	Q5_K_M	23.047 GB	large, very low quality loss - recommended
Python-Code-33B-Q6_K.gguf	Q6_K	26.687 GB	very large, extremely low quality loss
Python-Code-33B-Q8_0.gguf	Q8_0	34.565 GB	very large, extremely low quality loss - not recommended

Downloading instruction

Command line

Firstly, install Huggingface Client

pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

huggingface-cli download tensorblock/Python-Code-33B-GGUF --include "Python-Code-33B-Q2_K.gguf" --local-dir MY_LOCAL_DIR

If you wanna download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:

huggingface-cli download tensorblock/Python-Code-33B-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'