Instructions to use Arki05/BLS-Mini-Code-1.0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Arki05/BLS-Mini-Code-1.0-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Arki05/BLS-Mini-Code-1.0-GGUF",
	filename="BLS-Mini-Code-1.0-BF16-00001-of-00002.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 Arki05/BLS-Mini-Code-1.0-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Arki05/BLS-Mini-Code-1.0-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Arki05/BLS-Mini-Code-1.0-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Arki05/BLS-Mini-Code-1.0-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Arki05/BLS-Mini-Code-1.0-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 Arki05/BLS-Mini-Code-1.0-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Arki05/BLS-Mini-Code-1.0-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 Arki05/BLS-Mini-Code-1.0-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Arki05/BLS-Mini-Code-1.0-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Arki05/BLS-Mini-Code-1.0-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Arki05/BLS-Mini-Code-1.0-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Arki05/BLS-Mini-Code-1.0-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Arki05/BLS-Mini-Code-1.0-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Arki05/BLS-Mini-Code-1.0-GGUF:Q4_K_M

Ollama
How to use Arki05/BLS-Mini-Code-1.0-GGUF with Ollama:
```
ollama run hf.co/Arki05/BLS-Mini-Code-1.0-GGUF:Q4_K_M
```

Unsloth Studio

How to use Arki05/BLS-Mini-Code-1.0-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 Arki05/BLS-Mini-Code-1.0-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 Arki05/BLS-Mini-Code-1.0-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Arki05/BLS-Mini-Code-1.0-GGUF to start chatting

How to use Arki05/BLS-Mini-Code-1.0-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Arki05/BLS-Mini-Code-1.0-GGUF: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": "Arki05/BLS-Mini-Code-1.0-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Arki05/BLS-Mini-Code-1.0-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Arki05/BLS-Mini-Code-1.0-GGUF: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 Arki05/BLS-Mini-Code-1.0-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use Arki05/BLS-Mini-Code-1.0-GGUF with Docker Model Runner:
```
docker model run hf.co/Arki05/BLS-Mini-Code-1.0-GGUF:Q4_K_M
```

Lemonade

How to use Arki05/BLS-Mini-Code-1.0-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Arki05/BLS-Mini-Code-1.0-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.BLS-Mini-Code-1.0-GGUF-Q4_K_M

List all available models

lemonade list

Arki05 commited on about 11 hours ago

Commit

80349a8

verified ·

1 Parent(s): 5b82f42

Add full HumanEval results to all quants

Browse files

Files changed (1) hide show

README.md +17 -8

README.md CHANGED Viewed

@@ -35,15 +35,24 @@ fully held out from the imatrix calibration data — plus HumanEval/HumanEval+
 | file | size | PPL | mean KLD | top-1 % | HumanEval | HumanEval+ |
 |---|---|---|---|---|---|---|
 | BF16 (2 shards) | 61.0 GB | 7.7126 | — | — | | |
-| Q8_0 | 32.4 GB | 7.7356 | 0.007010 | 96.458 | | |
-| Q6_K | 25.1 GB | 7.7558 | 0.015611 | 94.602 | | |
 | Q5_K_M | 21.7 GB | 7.8333 | 0.020963 | 93.811 | 95.73 | 92.68 |
-| Q4_K_M | 18.6 GB | 7.9468 | 0.041855 | 91.342 | | |
-| IQ4_XS | 16.4 GB | 7.9794 | 0.049137 | 90.705 | | |
-| IQ3_M | 13.6 GB | 8.2776 | 0.112035 | 85.919 | | |
-| IQ2_M | 10.3 GB | 9.9756 | 0.283656 | 77.616 | | |
-| IQ2_XS | 9.2 GB | 11.0666 | 0.426120 | 73.339 | | |
-| IQ2_XXS | 8.3 GB | 12.6780 | 0.549859 | 69.743 | | |
 Recommendations: **Q5_K_M** if you have the memory (effectively lossless),
 **IQ4_XS** for the best size/quality ratio (matches Q4_K_M at -2.2 GB),

 | file | size | PPL | mean KLD | top-1 % | HumanEval | HumanEval+ |
 |---|---|---|---|---|---|---|
 | BF16 (2 shards) | 61.0 GB | 7.7126 | — | — | | |
+| Q8_0 | 32.4 GB | 7.7356 | 0.007010 | 96.458 | 92.07 | 89.02 |
+| Q6_K | 25.1 GB | 7.7558 | 0.015611 | 94.602 | 93.29 | 88.41 |
 | Q5_K_M | 21.7 GB | 7.8333 | 0.020963 | 93.811 | 95.73 | 92.68 |
+| Q4_K_M | 18.6 GB | 7.9468 | 0.041855 | 91.342 | 93.29 | 90.24 |
+| IQ4_XS | 16.4 GB | 7.9794 | 0.049137 | 90.705 | 92.68 | 88.41 |
+| IQ3_M | 13.6 GB | 8.2776 | 0.112035 | 85.919 | 90.85 | 87.20 |
+| IQ2_M | 10.3 GB | 9.9756 | 0.283656 | 77.616 | 84.15 | 79.88 |
+| IQ2_XS | 9.2 GB | 11.0666 | 0.426120 | 73.339 | 79.88 | 77.44 |
+| IQ2_XXS | 8.3 GB | 12.6780 | 0.549859 | 69.743 | 59.15 | 59.15 |
+HumanEval is pass@1 over 164 problems, so single-token greedy flips on a handful
+of problems move the score by a few points - read it as a sanity check, not a
+fine-grained ranking. The Q4-through-Q8 quants are statistically interchangeable
+on it (the spread is noise); **mean KLD and top-1 % are the reliable quality
+ordering**. The slope only becomes clear lower down: IQ3_M holds up, the IQ2 tier
+degrades visibly, and IQ2_XXS falls off a cliff (identical HumanEval/HumanEval+
+is the giveaway - it produces enough malformed code that the extra tests prune
+almost nothing further).
 Recommendations: **Q5_K_M** if you have the memory (effectively lossless),
 **IQ4_XS** for the best size/quality ratio (matches Q4_K_M at -2.2 GB),