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 Settings

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

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

Atomic Chat new
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 / README.md

legraphista

Upload README.md with huggingface_hub

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	---
	base_model: Bin12345/AutoCoder_S_6.7B
	inference: false
	library_name: gguf
	license: apache-2.0
	pipeline_tag: text-generation
	quantized_by: legraphista
	tags:
	- quantized
	- GGUF
	- imatrix
	- quantization
	- imat
	- imatrix
	- static
	---

	# AutoCoder_S_6.7B-IMat-GGUF
	_Llama.cpp imatrix quantization of Bin12345/AutoCoder_S_6.7B_

	Original Model: [Bin12345/AutoCoder_S_6.7B](https://huggingface.co/Bin12345/AutoCoder_S_6.7B)
	Original dtype: `BF16` (`bfloat16`)
	Quantized by: llama.cpp [b3010](https://github.com/ggerganov/llama.cpp/releases/tag/b3010)
	IMatrix dataset: [here](https://gist.githubusercontent.com/legraphista/d6d93f1a254bcfc58e0af3777eaec41e/raw/d380e7002cea4a51c33fffd47db851942754e7cc/imatrix.calibration.medium.raw)

	- [AutoCoder_S_6.7B-IMat-GGUF](#autocoder-s-6-7b-imat-gguf)
	- [Files](#files)
	- [IMatrix](#imatrix)
	- [Common Quants](#common-quants)
	- [All Quants](#all-quants)
	- [Downloading using huggingface-cli](#downloading-using-huggingface-cli)
	- [Inference](#inference)
	- [Simple chat template](#simple-chat-template)
	- [Chat template with system prompt](#chat-template-with-system-prompt)
	- [Llama.cpp](#llama-cpp)
	- [FAQ](#faq)
	- [Why is the IMatrix not applied everywhere?](#why-is-the-imatrix-not-applied-everywhere)
	- [How do I merge a split GGUF?](#how-do-i-merge-a-split-gguf)

	---

	## Files

	### IMatrix
	Status: ⏳ Processing
	Link: [here](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/imatrix.dat)

	### Common Quants
	\| Filename \| Quant type \| File Size \| Status \| Uses IMatrix \| Is Split \|
	\| -------- \| ---------- \| --------- \| ------ \| ------------ \| -------- \|
	\| [AutoCoder_S_6.7B.Q8_0.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.Q8_0.gguf) \| Q8_0 \| 7.16GB \| ✅ Available \| ⚪ Static \| 📦 No
	\| [AutoCoder_S_6.7B.Q6_K.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.Q6_K.gguf) \| Q6_K \| 5.53GB \| ✅ Available \| ⚪ Static \| 📦 No
	\| AutoCoder_S_6.7B.Q4_K \| Q4_K \| - \| ⏳ Processing \| 🟢 IMatrix \| -
	\| AutoCoder_S_6.7B.Q3_K \| Q3_K \| - \| ⏳ Processing \| 🟢 IMatrix \| -
	\| AutoCoder_S_6.7B.Q2_K \| Q2_K \| - \| ⏳ Processing \| 🟢 IMatrix \| -


	### All Quants
	\| Filename \| Quant type \| File Size \| Status \| Uses IMatrix \| Is Split \|
	\| -------- \| ---------- \| --------- \| ------ \| ------------ \| -------- \|
	\| [AutoCoder_S_6.7B.BF16.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.BF16.gguf) \| BF16 \| 13.48GB \| ✅ Available \| ⚪ Static \| 📦 No
	\| [AutoCoder_S_6.7B.FP16.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.FP16.gguf) \| F16 \| 13.48GB \| ✅ Available \| ⚪ Static \| 📦 No
	\| [AutoCoder_S_6.7B.Q5_K.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.Q5_K.gguf) \| Q5_K \| 4.79GB \| ✅ Available \| ⚪ Static \| 📦 No
	\| [AutoCoder_S_6.7B.Q5_K_S.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.Q5_K_S.gguf) \| Q5_K_S \| 4.65GB \| ✅ Available \| ⚪ Static \| 📦 No
	\| AutoCoder_S_6.7B.Q4_K_S \| Q4_K_S \| - \| ⏳ Processing \| 🟢 IMatrix \| -
	\| AutoCoder_S_6.7B.Q3_K_L \| Q3_K_L \| - \| ⏳ Processing \| 🟢 IMatrix \| -
	\| AutoCoder_S_6.7B.Q3_K_S \| Q3_K_S \| - \| ⏳ Processing \| 🟢 IMatrix \| -
	\| AutoCoder_S_6.7B.Q2_K_S \| Q2_K_S \| - \| ⏳ Processing \| 🟢 IMatrix \| -
	\| AutoCoder_S_6.7B.IQ4_NL \| IQ4_NL \| - \| ⏳ Processing \| 🟢 IMatrix \| -
	\| AutoCoder_S_6.7B.IQ4_XS \| IQ4_XS \| - \| ⏳ Processing \| 🟢 IMatrix \| -
	\| AutoCoder_S_6.7B.IQ3_M \| IQ3_M \| - \| ⏳ Processing \| 🟢 IMatrix \| -
	\| AutoCoder_S_6.7B.IQ3_S \| IQ3_S \| - \| ⏳ Processing \| 🟢 IMatrix \| -
	\| AutoCoder_S_6.7B.IQ3_XS \| IQ3_XS \| - \| ⏳ Processing \| 🟢 IMatrix \| -
	\| AutoCoder_S_6.7B.IQ3_XXS \| IQ3_XXS \| - \| ⏳ Processing \| 🟢 IMatrix \| -
	\| AutoCoder_S_6.7B.IQ2_M \| IQ2_M \| - \| ⏳ Processing \| 🟢 IMatrix \| -
	\| AutoCoder_S_6.7B.IQ2_S \| IQ2_S \| - \| ⏳ Processing \| 🟢 IMatrix \| -
	\| AutoCoder_S_6.7B.IQ2_XS \| IQ2_XS \| - \| ⏳ Processing \| 🟢 IMatrix \| -
	\| AutoCoder_S_6.7B.IQ2_XXS \| IQ2_XXS \| - \| ⏳ Processing \| 🟢 IMatrix \| -
	\| AutoCoder_S_6.7B.IQ1_M \| IQ1_M \| - \| ⏳ Processing \| 🟢 IMatrix \| -
	\| AutoCoder_S_6.7B.IQ1_S \| IQ1_S \| - \| ⏳ Processing \| 🟢 IMatrix \| -


	## 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](https://www.reddit.com/r/LocalLLaMA/comments/1993iro/ggufs_quants_can_punch_above_their_weights_now/), 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?
	1. 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`
	2. Locate your GGUF chunks folder (ex: `AutoCoder_S_6.7B.Q8_0`)
	3. 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](https://x.com/legraphista)!