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
- llama-cpp-python
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
File size: 9,346 Bytes
f666bc8 ef944b4 f666bc8 dd0b1ec 31c4466 79bb33e ef75f0d fcdc9e2 f666bc8 deb355d 310b3cc 50c6b8f 2667d36 7ec898a f31cd2c 5283511 8f16002 4718c5b 54a1ce3 243b851 391e1bb 56c4373 74122b5 0bcc91d 04d72fa d208378 3182bd9 f666bc8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 | ---
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: β
Available
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 | β
Available | βͺ Static | π¦ 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 | β
Available | βͺ Static | π¦ No
| [AutoCoder_S_6.7B.Q4_K.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.Q4_K.gguf) | Q4_K | 4.08GB | β
Available | π’ IMatrix | π¦ No
| [AutoCoder_S_6.7B.Q3_K.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.Q3_K.gguf) | Q3_K | 3.30GB | β
Available | π’ IMatrix | π¦ No
| [AutoCoder_S_6.7B.Q2_K.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.Q2_K.gguf) | Q2_K | 2.53GB | β
Available | π’ IMatrix | π¦ No
### 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 | β
Available | βͺ Static | π¦ 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 | β
Available | βͺ Static | π¦ 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 | β
Available | βͺ Static | π¦ 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 | β
Available | βͺ Static | π¦ No
| [AutoCoder_S_6.7B.Q4_K_S.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.Q4_K_S.gguf) | Q4_K_S | 3.86GB | β
Available | π’ IMatrix | π¦ No
| [AutoCoder_S_6.7B.Q3_K_L.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.Q3_K_L.gguf) | Q3_K_L | 3.60GB | β
Available | π’ IMatrix | π¦ No
| [AutoCoder_S_6.7B.Q3_K_S.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.Q3_K_S.gguf) | Q3_K_S | 2.95GB | β
Available | π’ IMatrix | π¦ No
| [AutoCoder_S_6.7B.Q2_K_S.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.Q2_K_S.gguf) | Q2_K_S | 2.32GB | β
Available | π’ IMatrix | π¦ No
| [AutoCoder_S_6.7B.IQ4_NL.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.IQ4_NL.gguf) | IQ4_NL | 3.83GB | β
Available | π’ IMatrix | π¦ No
| [AutoCoder_S_6.7B.IQ4_XS.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.IQ4_XS.gguf) | IQ4_XS | 3.62GB | β
Available | π’ IMatrix | π¦ No
| [AutoCoder_S_6.7B.IQ3_M.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.IQ3_M.gguf) | IQ3_M | 3.12GB | β
Available | π’ IMatrix | π¦ No
| [AutoCoder_S_6.7B.IQ3_S.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.IQ3_S.gguf) | IQ3_S | 2.95GB | β
Available | π’ IMatrix | π¦ No
| [AutoCoder_S_6.7B.IQ3_XS.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.IQ3_XS.gguf) | IQ3_XS | 2.80GB | β
Available | π’ IMatrix | π¦ No
| [AutoCoder_S_6.7B.IQ3_XXS.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.IQ3_XXS.gguf) | IQ3_XXS | 2.59GB | β
Available | π’ IMatrix | π¦ No
| [AutoCoder_S_6.7B.IQ2_M.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.IQ2_M.gguf) | IQ2_M | 2.36GB | β
Available | π’ IMatrix | π¦ No
| [AutoCoder_S_6.7B.IQ2_S.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.IQ2_S.gguf) | IQ2_S | 2.20GB | β
Available | π’ IMatrix | π¦ No
| [AutoCoder_S_6.7B.IQ2_XS.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.IQ2_XS.gguf) | IQ2_XS | 2.04GB | β
Available | π’ IMatrix | π¦ No
| [AutoCoder_S_6.7B.IQ2_XXS.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.IQ2_XXS.gguf) | IQ2_XXS | 1.86GB | β
Available | π’ IMatrix | π¦ No
| [AutoCoder_S_6.7B.IQ1_M.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.IQ1_M.gguf) | IQ1_M | 1.65GB | β
Available | π’ IMatrix | π¦ No
| [AutoCoder_S_6.7B.IQ1_S.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.IQ1_S.gguf) | IQ1_S | 1.53GB | β
Available | π’ IMatrix | π¦ 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.<ο½endβofβsentenceο½>
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.<ο½endβofβsentenceο½>
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.
---
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