Instructions to use afrideva/smol_llama-101M-GQA-python-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use afrideva/smol_llama-101M-GQA-python-GGUF 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 afrideva/smol_llama-101M-GQA-python-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf afrideva/smol_llama-101M-GQA-python-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf afrideva/smol_llama-101M-GQA-python-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf afrideva/smol_llama-101M-GQA-python-GGUF: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 afrideva/smol_llama-101M-GQA-python-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf afrideva/smol_llama-101M-GQA-python-GGUF: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 afrideva/smol_llama-101M-GQA-python-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf afrideva/smol_llama-101M-GQA-python-GGUF:Q4_K_M

Use Docker

docker model run hf.co/afrideva/smol_llama-101M-GQA-python-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use afrideva/smol_llama-101M-GQA-python-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "afrideva/smol_llama-101M-GQA-python-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "afrideva/smol_llama-101M-GQA-python-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/afrideva/smol_llama-101M-GQA-python-GGUF:Q4_K_M

Ollama
How to use afrideva/smol_llama-101M-GQA-python-GGUF with Ollama:
```
ollama run hf.co/afrideva/smol_llama-101M-GQA-python-GGUF:Q4_K_M
```

Unsloth Studio

How to use afrideva/smol_llama-101M-GQA-python-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 afrideva/smol_llama-101M-GQA-python-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 afrideva/smol_llama-101M-GQA-python-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for afrideva/smol_llama-101M-GQA-python-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use afrideva/smol_llama-101M-GQA-python-GGUF with Docker Model Runner:
```
docker model run hf.co/afrideva/smol_llama-101M-GQA-python-GGUF:Q4_K_M
```

Lemonade

How to use afrideva/smol_llama-101M-GQA-python-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull afrideva/smol_llama-101M-GQA-python-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.smol_llama-101M-GQA-python-GGUF-Q4_K_M

List all available models

lemonade list

afrideva commited on Dec 16, 2023

Commit

f02032c

1 Parent(s): 43630de

Upload README.md with huggingface_hub

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README.md +11 -7

README.md CHANGED Viewed

@@ -77,19 +77,23 @@ Quantized GGUF model files for [smol_llama-101M-GQA-python](https://huggingface.
 | Name | Quant method | Size |
 | ---- | ---- | ---- |
-| [smol_llama-101m-gqa-python.fp16.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-python-GGUF/resolve/main/smol_llama-101m-gqa-python.fp16.gguf) | fp16 | None  |
-| [smol_llama-101m-gqa-python.q2_k.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-python-GGUF/resolve/main/smol_llama-101m-gqa-python.q2_k.gguf) | q2_k | None  |
-| [smol_llama-101m-gqa-python.q3_k_m.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-python-GGUF/resolve/main/smol_llama-101m-gqa-python.q3_k_m.gguf) | q3_k_m | None  |
-| [smol_llama-101m-gqa-python.q4_k_m.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-python-GGUF/resolve/main/smol_llama-101m-gqa-python.q4_k_m.gguf) | q4_k_m | None  |
-| [smol_llama-101m-gqa-python.q5_k_m.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-python-GGUF/resolve/main/smol_llama-101m-gqa-python.q5_k_m.gguf) | q5_k_m | None  |
-| [smol_llama-101m-gqa-python.q6_k.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-python-GGUF/resolve/main/smol_llama-101m-gqa-python.q6_k.gguf) | q6_k | None  |
-| [smol_llama-101m-gqa-python.q8_0.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-python-GGUF/resolve/main/smol_llama-101m-gqa-python.q8_0.gguf) | q8_0 | None  |
 ## Original Model Card:
 # smol_llama-101M-GQA: python
 > 400MB of buzz: pure Python programming nectar! 🍯
 This model is the general pre-trained checkpoint `BEE-spoke-data/smol_llama-101M-GQA` trained on a deduped version of `pypi` for +1 epoch. Play with the model in [this demo space](https://huggingface.co/spaces/BEE-spoke-data/beecoder-playground).

 | Name | Quant method | Size |
 | ---- | ---- | ---- |
+| [smol_llama-101m-gqa-python.fp16.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-python-GGUF/resolve/main/smol_llama-101m-gqa-python.fp16.gguf) | fp16 | 203.28 MB  |
+| [smol_llama-101m-gqa-python.q2_k.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-python-GGUF/resolve/main/smol_llama-101m-gqa-python.q2_k.gguf) | q2_k | 50.93 MB  |
+| [smol_llama-101m-gqa-python.q3_k_m.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-python-GGUF/resolve/main/smol_llama-101m-gqa-python.q3_k_m.gguf) | q3_k_m | 57.06 MB  |
+| [smol_llama-101m-gqa-python.q4_k_m.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-python-GGUF/resolve/main/smol_llama-101m-gqa-python.q4_k_m.gguf) | q4_k_m | 65.41 MB  |
+| [smol_llama-101m-gqa-python.q5_k_m.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-python-GGUF/resolve/main/smol_llama-101m-gqa-python.q5_k_m.gguf) | q5_k_m | 74.34 MB  |
+| [smol_llama-101m-gqa-python.q6_k.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-python-GGUF/resolve/main/smol_llama-101m-gqa-python.q6_k.gguf) | q6_k | 83.83 MB  |
+| [smol_llama-101m-gqa-python.q8_0.gguf](https://huggingface.co/afrideva/smol_llama-101M-GQA-python-GGUF/resolve/main/smol_llama-101m-gqa-python.q8_0.gguf) | q8_0 | 108.35 MB  |
 ## Original Model Card:
 # smol_llama-101M-GQA: python
+<a href="https://colab.research.google.com/gist/pszemraj/91b5a267df95461b46922e6c0212e8f7/beecoder-basic-test-notebook.ipynb">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
+</a>
 > 400MB of buzz: pure Python programming nectar! 🍯
 This model is the general pre-trained checkpoint `BEE-spoke-data/smol_llama-101M-GQA` trained on a deduped version of `pypi` for +1 epoch. Play with the model in [this demo space](https://huggingface.co/spaces/BEE-spoke-data/beecoder-playground).