Instructions to use TheBloke/Camel-Platypus2-70B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TheBloke/Camel-Platypus2-70B-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("TheBloke/Camel-Platypus2-70B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use TheBloke/Camel-Platypus2-70B-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 TheBloke/Camel-Platypus2-70B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf TheBloke/Camel-Platypus2-70B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf TheBloke/Camel-Platypus2-70B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf TheBloke/Camel-Platypus2-70B-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 TheBloke/Camel-Platypus2-70B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf TheBloke/Camel-Platypus2-70B-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 TheBloke/Camel-Platypus2-70B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf TheBloke/Camel-Platypus2-70B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/TheBloke/Camel-Platypus2-70B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use TheBloke/Camel-Platypus2-70B-GGUF with Ollama:
```
ollama run hf.co/TheBloke/Camel-Platypus2-70B-GGUF:Q4_K_M
```

Unsloth Studio

How to use TheBloke/Camel-Platypus2-70B-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 TheBloke/Camel-Platypus2-70B-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 TheBloke/Camel-Platypus2-70B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for TheBloke/Camel-Platypus2-70B-GGUF to start chatting

Docker Model Runner
How to use TheBloke/Camel-Platypus2-70B-GGUF with Docker Model Runner:
```
docker model run hf.co/TheBloke/Camel-Platypus2-70B-GGUF:Q4_K_M
```

Lemonade

How to use TheBloke/Camel-Platypus2-70B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull TheBloke/Camel-Platypus2-70B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Camel-Platypus2-70B-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

TheBloke commited on Sep 7, 2023

Commit

cc8f4d8

1 Parent(s): 0270e63

Upload README.md

Browse files

Files changed (1) hide show

README.md +8 -12

README.md CHANGED Viewed

@@ -46,13 +46,13 @@ The key benefit of GGUF is that it is a extensible, future-proof format which st
 Here are a list of clients and libraries that are known to support GGUF:
 * [llama.cpp](https://github.com/ggerganov/llama.cpp).
-* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI. Supports GGUF with GPU acceleration via the ctransformers backend - llama-cpp-python backend should work soon too.
-* [KoboldCpp](https://github.com/LostRuins/koboldcpp), now supports GGUF as of release 1.41! A powerful GGML web UI, with full GPU accel. Especially good for story telling.
-* [LM Studio](https://lmstudio.ai/), version 0.2.2 and later support GGUF. A fully featured local GUI with GPU acceleration on both Windows (NVidia and AMD), and macOS.
-* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), should now work, choose the `c_transformers` backend. A great web UI with many interesting features. Supports CUDA GPU acceleration.
-* [ctransformers](https://github.com/marella/ctransformers), now supports GGUF as of version 0.2.24! A Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
-* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), supports GGUF as of version 0.1.79. A Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
-* [candle](https://github.com/huggingface/candle), added GGUF support on August 22nd. Candle is a Rust ML framework with a focus on performance, including GPU support, and ease of use.
 <!-- README_GGUF.md-about-gguf end -->
 <!-- repositories-available start -->
@@ -105,14 +105,10 @@ Refer to the Provided Files table below to see what files use which methods, and
 | Name | Quant method | Bits | Size | Max RAM required | Use case |
 | ---- | ---- | ---- | ---- | ---- | ----- |
-| [camel-platypus2-70b.Q6_K.gguf-split-b](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GGUF/blob/main/camel-platypus2-70b.Q6_K.gguf-split-b) | Q6_K | 6 | 19.89 GB| 22.39 GB | very large, extremely low quality loss |
 | [camel-platypus2-70b.Q2_K.gguf](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GGUF/blob/main/camel-platypus2-70b.Q2_K.gguf) | Q2_K | 2 | 29.28 GB| 31.78 GB | smallest, significant quality loss - not recommended for most purposes |
 | [camel-platypus2-70b.Q3_K_S.gguf](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GGUF/blob/main/camel-platypus2-70b.Q3_K_S.gguf) | Q3_K_S | 3 | 29.92 GB| 32.42 GB | very small, high quality loss |
 | [camel-platypus2-70b.Q3_K_M.gguf](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GGUF/blob/main/camel-platypus2-70b.Q3_K_M.gguf) | Q3_K_M | 3 | 33.19 GB| 35.69 GB | very small, high quality loss |
 | [camel-platypus2-70b.Q3_K_L.gguf](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GGUF/blob/main/camel-platypus2-70b.Q3_K_L.gguf) | Q3_K_L | 3 | 36.15 GB| 38.65 GB | small, substantial quality loss |
-| [camel-platypus2-70b.Q8_0.gguf-split-b](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GGUF/blob/main/camel-platypus2-70b.Q8_0.gguf-split-b) | Q8_0 | 8 | 36.59 GB| 39.09 GB | very large, extremely low quality loss - not recommended |
-| [camel-platypus2-70b.Q6_K.gguf-split-a](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GGUF/blob/main/camel-platypus2-70b.Q6_K.gguf-split-a) | Q6_K | 6 | 36.70 GB| 39.20 GB | very large, extremely low quality loss |
-| [camel-platypus2-70b.Q8_0.gguf-split-a](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GGUF/blob/main/camel-platypus2-70b.Q8_0.gguf-split-a) | Q8_0 | 8 | 36.70 GB| 39.20 GB | very large, extremely low quality loss - not recommended |
 | [camel-platypus2-70b.Q4_0.gguf](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GGUF/blob/main/camel-platypus2-70b.Q4_0.gguf) | Q4_0 | 4 | 38.87 GB| 41.37 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
 | [camel-platypus2-70b.Q4_K_S.gguf](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GGUF/blob/main/camel-platypus2-70b.Q4_K_S.gguf) | Q4_K_S | 4 | 39.07 GB| 41.57 GB | small, greater quality loss |
 | [camel-platypus2-70b.Q4_K_M.gguf](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GGUF/blob/main/camel-platypus2-70b.Q4_K_M.gguf) | Q4_K_M | 4 | 41.42 GB| 43.92 GB | medium, balanced quality - recommended |
@@ -168,7 +164,7 @@ Make sure you are using `llama.cpp` from commit [6381d4e110bd0ec02843a60bbeb8b6f
 For compatibility with older versions of llama.cpp, or for any third-party libraries or clients that haven't yet updated for GGUF, please use GGML files instead.
 ```
-./main -t 10 -ngl 32 -m camel-platypus2-70b.q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\nWrite a story about llamas\n\n### Response:"
 ```
 Change `-t 10` to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use `-t 8`. If offloading all layers to GPU, set `-t 1`.

 Here are a list of clients and libraries that are known to support GGUF:
 * [llama.cpp](https://github.com/ggerganov/llama.cpp).
+* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions.
+* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with full GPU accel across multiple platforms and GPU architectures. Especially good for story telling.
+* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI with GPU acceleration on both Windows (NVidia and AMD), and macOS.
+* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
+* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
+* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
+* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
 <!-- README_GGUF.md-about-gguf end -->
 <!-- repositories-available start -->
 | Name | Quant method | Bits | Size | Max RAM required | Use case |
 | ---- | ---- | ---- | ---- | ---- | ----- |
 | [camel-platypus2-70b.Q2_K.gguf](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GGUF/blob/main/camel-platypus2-70b.Q2_K.gguf) | Q2_K | 2 | 29.28 GB| 31.78 GB | smallest, significant quality loss - not recommended for most purposes |
 | [camel-platypus2-70b.Q3_K_S.gguf](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GGUF/blob/main/camel-platypus2-70b.Q3_K_S.gguf) | Q3_K_S | 3 | 29.92 GB| 32.42 GB | very small, high quality loss |
 | [camel-platypus2-70b.Q3_K_M.gguf](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GGUF/blob/main/camel-platypus2-70b.Q3_K_M.gguf) | Q3_K_M | 3 | 33.19 GB| 35.69 GB | very small, high quality loss |
 | [camel-platypus2-70b.Q3_K_L.gguf](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GGUF/blob/main/camel-platypus2-70b.Q3_K_L.gguf) | Q3_K_L | 3 | 36.15 GB| 38.65 GB | small, substantial quality loss |
 | [camel-platypus2-70b.Q4_0.gguf](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GGUF/blob/main/camel-platypus2-70b.Q4_0.gguf) | Q4_0 | 4 | 38.87 GB| 41.37 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
 | [camel-platypus2-70b.Q4_K_S.gguf](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GGUF/blob/main/camel-platypus2-70b.Q4_K_S.gguf) | Q4_K_S | 4 | 39.07 GB| 41.57 GB | small, greater quality loss |
 | [camel-platypus2-70b.Q4_K_M.gguf](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GGUF/blob/main/camel-platypus2-70b.Q4_K_M.gguf) | Q4_K_M | 4 | 41.42 GB| 43.92 GB | medium, balanced quality - recommended |
 For compatibility with older versions of llama.cpp, or for any third-party libraries or clients that haven't yet updated for GGUF, please use GGML files instead.
 ```
+./main -t 10 -ngl 32 -m camel-platypus2-70b.q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:"
 ```
 Change `-t 10` to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use `-t 8`. If offloading all layers to GPU, set `-t 1`.