Instructions to use legraphista/AutoCoder_S_6.7B-IMat-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use legraphista/AutoCoder_S_6.7B-IMat-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="legraphista/AutoCoder_S_6.7B-IMat-GGUF",
	filename="AutoCoder_S_6.7B.BF16.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 legraphista/AutoCoder_S_6.7B-IMat-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf legraphista/AutoCoder_S_6.7B-IMat-GGUF:Q4_K_S
# Run inference directly in the terminal:
llama-cli -hf legraphista/AutoCoder_S_6.7B-IMat-GGUF:Q4_K_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf legraphista/AutoCoder_S_6.7B-IMat-GGUF:Q4_K_S
# Run inference directly in the terminal:
llama-cli -hf legraphista/AutoCoder_S_6.7B-IMat-GGUF:Q4_K_S

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 legraphista/AutoCoder_S_6.7B-IMat-GGUF:Q4_K_S
# Run inference directly in the terminal:
./llama-cli -hf legraphista/AutoCoder_S_6.7B-IMat-GGUF:Q4_K_S

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 legraphista/AutoCoder_S_6.7B-IMat-GGUF:Q4_K_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf legraphista/AutoCoder_S_6.7B-IMat-GGUF:Q4_K_S

Use Docker

docker model run hf.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF:Q4_K_S

LM Studio
Jan

vLLM

How to use legraphista/AutoCoder_S_6.7B-IMat-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "legraphista/AutoCoder_S_6.7B-IMat-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": "legraphista/AutoCoder_S_6.7B-IMat-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF:Q4_K_S

Ollama
How to use legraphista/AutoCoder_S_6.7B-IMat-GGUF with Ollama:
```
ollama run hf.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF:Q4_K_S
```

Unsloth Studio new

How to use legraphista/AutoCoder_S_6.7B-IMat-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 legraphista/AutoCoder_S_6.7B-IMat-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 legraphista/AutoCoder_S_6.7B-IMat-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for legraphista/AutoCoder_S_6.7B-IMat-GGUF to start chatting

Docker Model Runner
How to use legraphista/AutoCoder_S_6.7B-IMat-GGUF with Docker Model Runner:
```
docker model run hf.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF:Q4_K_S
```

Lemonade

How to use legraphista/AutoCoder_S_6.7B-IMat-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull legraphista/AutoCoder_S_6.7B-IMat-GGUF:Q4_K_S

Run and chat with the model

lemonade run user.AutoCoder_S_6.7B-IMat-GGUF-Q4_K_S

List all available models

lemonade list

AutoCoder_S_6.7B-IMat-GGUF

Llama.cpp imatrix quantization of Bin12345/AutoCoder_S_6.7B

Original Model: Bin12345/AutoCoder_S_6.7B
Original dtype: BF16 (bfloat16)
Quantized by: llama.cpp b3010
IMatrix dataset: here

AutoCoder_S_6.7B-IMat-GGUF

Files

IMatrix

Status: ✅ Available
Link: here

Common Quants

Filename	Quant type	File Size	Status	Uses IMatrix	Is Split
AutoCoder_S_6.7B.Q8_0.gguf	Q8_0	7.16GB	✅ Available	⚪ Static	📦 No
AutoCoder_S_6.7B.Q6_K.gguf	Q6_K	5.53GB	✅ Available	⚪ Static	📦 No
AutoCoder_S_6.7B.Q4_K.gguf	Q4_K	4.08GB	✅ Available	🟢 IMatrix	📦 No
AutoCoder_S_6.7B.Q3_K.gguf	Q3_K	3.30GB	✅ Available	🟢 IMatrix	📦 No
AutoCoder_S_6.7B.Q2_K.gguf	Q2_K	2.53GB	✅ Available	🟢 IMatrix	📦 No

All Quants

Filename	Quant type	File Size	Status	Uses IMatrix	Is Split
AutoCoder_S_6.7B.BF16.gguf	BF16	13.48GB	✅ Available	⚪ Static	📦 No
AutoCoder_S_6.7B.FP16.gguf	F16	13.48GB	✅ Available	⚪ Static	📦 No
AutoCoder_S_6.7B.Q5_K.gguf	Q5_K	4.79GB	✅ Available	⚪ Static	📦 No
AutoCoder_S_6.7B.Q5_K_S.gguf	Q5_K_S	4.65GB	✅ Available	⚪ Static	📦 No
AutoCoder_S_6.7B.Q4_K_S.gguf	Q4_K_S	3.86GB	✅ Available	🟢 IMatrix	📦 No
AutoCoder_S_6.7B.Q3_K_L.gguf	Q3_K_L	3.60GB	✅ Available	🟢 IMatrix	📦 No
AutoCoder_S_6.7B.Q3_K_S.gguf	Q3_K_S	2.95GB	✅ Available	🟢 IMatrix	📦 No
AutoCoder_S_6.7B.Q2_K_S.gguf	Q2_K_S	2.32GB	✅ Available	🟢 IMatrix	📦 No
AutoCoder_S_6.7B.IQ4_NL.gguf	IQ4_NL	3.83GB	✅ Available	🟢 IMatrix	📦 No
AutoCoder_S_6.7B.IQ4_XS.gguf	IQ4_XS	3.62GB	✅ Available	🟢 IMatrix	📦 No
AutoCoder_S_6.7B.IQ3_M.gguf	IQ3_M	3.12GB	✅ Available	🟢 IMatrix	📦 No
AutoCoder_S_6.7B.IQ3_S.gguf	IQ3_S	2.95GB	✅ Available	🟢 IMatrix	📦 No
AutoCoder_S_6.7B.IQ3_XS.gguf	IQ3_XS	2.80GB	✅ Available	🟢 IMatrix	📦 No
AutoCoder_S_6.7B.IQ3_XXS.gguf	IQ3_XXS	2.59GB	✅ Available	🟢 IMatrix	📦 No
AutoCoder_S_6.7B.IQ2_M.gguf	IQ2_M	2.36GB	✅ Available	🟢 IMatrix	📦 No
AutoCoder_S_6.7B.IQ2_S.gguf	IQ2_S	2.20GB	✅ Available	🟢 IMatrix	📦 No
AutoCoder_S_6.7B.IQ2_XS.gguf	IQ2_XS	2.04GB	✅ Available	🟢 IMatrix	📦 No
AutoCoder_S_6.7B.IQ2_XXS.gguf	IQ2_XXS	1.86GB	✅ Available	🟢 IMatrix	📦 No
AutoCoder_S_6.7B.IQ1_M.gguf	IQ1_M	1.65GB	✅ Available	🟢 IMatrix	📦 No
AutoCoder_S_6.7B.IQ1_S.gguf	IQ1_S	1.53GB	✅ Available	🟢 IMatrix	📦 No

Downloading using huggingface-cli

If you do not have hugginface-cli installed:

pip install -U "huggingface_hub[cli]"

Download the specific file you want:

huggingface-cli download legraphista/AutoCoder_S_6.7B-IMat-GGUF --include "AutoCoder_S_6.7B.Q8_0.gguf" --local-dir ./

If the model file is big, it has been split into multiple files. In order to download them all to a local folder, run:

huggingface-cli download legraphista/AutoCoder_S_6.7B-IMat-GGUF --include "AutoCoder_S_6.7B.Q8_0/*" --local-dir ./
# see FAQ for merging GGUF's

Inference

Simple chat template

Human: Can you provide ways to eat combinations of bananas and dragonfruits?
Assistant: Sure! Here are some ways to eat bananas and dragonfruits together:
 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey.
 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey.<｜end▁of▁sentence｜>
Human: What about solving an 2x + 3 = 7 equation?
Assistant:

Chat template with system prompt

You are a helpful AI.
Human: Can you provide ways to eat combinations of bananas and dragonfruits?
Assistant: Sure! Here are some ways to eat bananas and dragonfruits together:
 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey.
 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey.<｜end▁of▁sentence｜>
Human: What about solving an 2x + 3 = 7 equation?
Assistant:

Llama.cpp

llama.cpp/main -m AutoCoder_S_6.7B.Q8_0.gguf --color -i -p "prompt here (according to the chat template)"

FAQ

Why is the IMatrix not applied everywhere?

According to this investigation, it appears that lower quantizations are the only ones that benefit from the imatrix input (as per hellaswag results).

How do I merge a split GGUF?

Make sure you have gguf-split available
- To get hold of gguf-split, navigate to https://github.com/ggerganov/llama.cpp/releases
- Download the appropriate zip for your system from the latest release
- Unzip the archive and you should be able to find gguf-split
Locate your GGUF chunks folder (ex: AutoCoder_S_6.7B.Q8_0)
Run gguf-split --merge AutoCoder_S_6.7B.Q8_0/AutoCoder_S_6.7B.Q8_0-00001-of-XXXXX.gguf AutoCoder_S_6.7B.Q8_0.gguf
- Make sure to point gguf-split to the first chunk of the split.

Got a suggestion? Ping me @legraphista!

Downloads last month: 429

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

View +1 variant

Model tree for legraphista/AutoCoder_S_6.7B-IMat-GGUF

Base model

Bin12345/AutoCoder_S_6.7B

Quantized

(3)

this model

Collection including legraphista/AutoCoder_S_6.7B-IMat-GGUF

AutoCoder

Collection

2 items • Updated Aug 5, 2024