Instructions to use tensorblock/model-llama-dec21-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tensorblock/model-llama-dec21-GGUF with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("tensorblock/model-llama-dec21-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use tensorblock/model-llama-dec21-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 tensorblock/model-llama-dec21-GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf tensorblock/model-llama-dec21-GGUF:Q2_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf tensorblock/model-llama-dec21-GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf tensorblock/model-llama-dec21-GGUF:Q2_K
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 tensorblock/model-llama-dec21-GGUF:Q2_K # Run inference directly in the terminal: ./llama-cli -hf tensorblock/model-llama-dec21-GGUF:Q2_K
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 tensorblock/model-llama-dec21-GGUF:Q2_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf tensorblock/model-llama-dec21-GGUF:Q2_K
Use Docker
docker model run hf.co/tensorblock/model-llama-dec21-GGUF:Q2_K
- LM Studio
- Jan
- Ollama
How to use tensorblock/model-llama-dec21-GGUF with Ollama:
ollama run hf.co/tensorblock/model-llama-dec21-GGUF:Q2_K
- Unsloth Desktop
- Docker Model Runner
How to use tensorblock/model-llama-dec21-GGUF with Docker Model Runner:
docker model run hf.co/tensorblock/model-llama-dec21-GGUF:Q2_K
- Lemonade
How to use tensorblock/model-llama-dec21-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tensorblock/model-llama-dec21-GGUF:Q2_K
Run and chat with the model
lemonade run user.model-llama-dec21-GGUF-Q2_K
List all available models
lemonade list
- Atomic Chat
Upload folder using huggingface_hub
Browse files- .gitattributes +12 -0
- README.md +80 -0
- model-llama-dec21-Q2_K.gguf +3 -0
- model-llama-dec21-Q3_K_L.gguf +3 -0
- model-llama-dec21-Q3_K_M.gguf +3 -0
- model-llama-dec21-Q3_K_S.gguf +3 -0
- model-llama-dec21-Q4_0.gguf +3 -0
- model-llama-dec21-Q4_K_M.gguf +3 -0
- model-llama-dec21-Q4_K_S.gguf +3 -0
- model-llama-dec21-Q5_0.gguf +3 -0
- model-llama-dec21-Q5_K_M.gguf +3 -0
- model-llama-dec21-Q5_K_S.gguf +3 -0
- model-llama-dec21-Q6_K.gguf +3 -0
- model-llama-dec21-Q8_0.gguf +3 -0
.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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model-llama-dec21-Q2_K.gguf filter=lfs diff=lfs merge=lfs -text
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model-llama-dec21-Q3_K_L.gguf filter=lfs diff=lfs merge=lfs -text
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model-llama-dec21-Q3_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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model-llama-dec21-Q3_K_S.gguf filter=lfs diff=lfs merge=lfs -text
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model-llama-dec21-Q4_0.gguf filter=lfs diff=lfs merge=lfs -text
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model-llama-dec21-Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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model-llama-dec21-Q4_K_S.gguf filter=lfs diff=lfs merge=lfs -text
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model-llama-dec21-Q5_0.gguf filter=lfs diff=lfs merge=lfs -text
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model-llama-dec21-Q5_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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model-llama-dec21-Q5_K_S.gguf filter=lfs diff=lfs merge=lfs -text
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model-llama-dec21-Q6_K.gguf filter=lfs diff=lfs merge=lfs -text
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model-llama-dec21-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
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README.md
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| 1 |
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---
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| 2 |
+
library_name: transformers
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+
tags:
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| 4 |
+
- TensorBlock
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| 5 |
+
- GGUF
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| 6 |
+
base_model: yfarm01/model-llama-dec21
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| 7 |
+
---
|
| 8 |
+
|
| 9 |
+
<div style="width: auto; margin-left: auto; margin-right: auto">
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| 10 |
+
<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
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| 11 |
+
</div>
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| 12 |
+
<div style="display: flex; justify-content: space-between; width: 100%;">
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| 13 |
+
<div style="display: flex; flex-direction: column; align-items: flex-start;">
|
| 14 |
+
<p style="margin-top: 0.5em; margin-bottom: 0em;">
|
| 15 |
+
Feedback and support: TensorBlock's <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a>
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| 16 |
+
</p>
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| 17 |
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</div>
|
| 18 |
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</div>
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| 19 |
+
|
| 20 |
+
## yfarm01/model-llama-dec21 - GGUF
|
| 21 |
+
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This repo contains GGUF format model files for [yfarm01/model-llama-dec21](https://huggingface.co/yfarm01/model-llama-dec21).
|
| 23 |
+
|
| 24 |
+
The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4242](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).
|
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+
|
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<div style="text-align: left; margin: 20px 0;">
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<a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
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| 28 |
+
Run them on the TensorBlock client using your local machine ↗
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+
</a>
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| 30 |
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</div>
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|
| 32 |
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## Prompt template
|
| 33 |
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```
|
| 35 |
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<|im_start|>system
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| 36 |
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{system_prompt}<|im_end|>
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| 37 |
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<|im_start|>user
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| 38 |
+
{prompt}<|im_end|>
|
| 39 |
+
<|im_start|>assistant
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| 40 |
+
```
|
| 41 |
+
|
| 42 |
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## Model file specification
|
| 43 |
+
|
| 44 |
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| Filename | Quant type | File Size | Description |
|
| 45 |
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| -------- | ---------- | --------- | ----------- |
|
| 46 |
+
| [model-llama-dec21-Q2_K.gguf](https://huggingface.co/tensorblock/model-llama-dec21-GGUF/blob/main/model-llama-dec21-Q2_K.gguf) | Q2_K | 3.778 GB | smallest, significant quality loss - not recommended for most purposes |
|
| 47 |
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| [model-llama-dec21-Q3_K_S.gguf](https://huggingface.co/tensorblock/model-llama-dec21-GGUF/blob/main/model-llama-dec21-Q3_K_S.gguf) | Q3_K_S | 4.335 GB | very small, high quality loss |
|
| 48 |
+
| [model-llama-dec21-Q3_K_M.gguf](https://huggingface.co/tensorblock/model-llama-dec21-GGUF/blob/main/model-llama-dec21-Q3_K_M.gguf) | Q3_K_M | 4.712 GB | very small, high quality loss |
|
| 49 |
+
| [model-llama-dec21-Q3_K_L.gguf](https://huggingface.co/tensorblock/model-llama-dec21-GGUF/blob/main/model-llama-dec21-Q3_K_L.gguf) | Q3_K_L | 4.929 GB | small, substantial quality loss |
|
| 50 |
+
| [model-llama-dec21-Q4_0.gguf](https://huggingface.co/tensorblock/model-llama-dec21-GGUF/blob/main/model-llama-dec21-Q4_0.gguf) | Q4_0 | 5.169 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
|
| 51 |
+
| [model-llama-dec21-Q4_K_S.gguf](https://huggingface.co/tensorblock/model-llama-dec21-GGUF/blob/main/model-llama-dec21-Q4_K_S.gguf) | Q4_K_S | 5.473 GB | small, greater quality loss |
|
| 52 |
+
| [model-llama-dec21-Q4_K_M.gguf](https://huggingface.co/tensorblock/model-llama-dec21-GGUF/blob/main/model-llama-dec21-Q4_K_M.gguf) | Q4_K_M | 5.875 GB | medium, balanced quality - recommended |
|
| 53 |
+
| [model-llama-dec21-Q5_0.gguf](https://huggingface.co/tensorblock/model-llama-dec21-GGUF/blob/main/model-llama-dec21-Q5_0.gguf) | Q5_0 | 6.242 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
|
| 54 |
+
| [model-llama-dec21-Q5_K_S.gguf](https://huggingface.co/tensorblock/model-llama-dec21-GGUF/blob/main/model-llama-dec21-Q5_K_S.gguf) | Q5_K_S | 6.386 GB | large, low quality loss - recommended |
|
| 55 |
+
| [model-llama-dec21-Q5_K_M.gguf](https://huggingface.co/tensorblock/model-llama-dec21-GGUF/blob/main/model-llama-dec21-Q5_K_M.gguf) | Q5_K_M | 6.729 GB | large, very low quality loss - recommended |
|
| 56 |
+
| [model-llama-dec21-Q6_K.gguf](https://huggingface.co/tensorblock/model-llama-dec21-GGUF/blob/main/model-llama-dec21-Q6_K.gguf) | Q6_K | 7.939 GB | very large, extremely low quality loss |
|
| 57 |
+
| [model-llama-dec21-Q8_0.gguf](https://huggingface.co/tensorblock/model-llama-dec21-GGUF/blob/main/model-llama-dec21-Q8_0.gguf) | Q8_0 | 9.559 GB | very large, extremely low quality loss - not recommended |
|
| 58 |
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|
| 59 |
+
|
| 60 |
+
## Downloading instruction
|
| 61 |
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| 62 |
+
### Command line
|
| 63 |
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| 64 |
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Firstly, install Huggingface Client
|
| 65 |
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| 66 |
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```shell
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| 67 |
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pip install -U "huggingface_hub[cli]"
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| 68 |
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```
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| 69 |
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Then, downoad the individual model file the a local directory
|
| 71 |
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| 72 |
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```shell
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| 73 |
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huggingface-cli download tensorblock/model-llama-dec21-GGUF --include "model-llama-dec21-Q2_K.gguf" --local-dir MY_LOCAL_DIR
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| 74 |
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```
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If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:
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| 77 |
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| 78 |
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```shell
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| 79 |
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huggingface-cli download tensorblock/model-llama-dec21-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
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```
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