Text Generation
Transformers
GGUF
English
tenyx-fine-tuning
dpo
tenyxchat
llama3
TensorBlock
GGUF
conversational
Instructions to use tensorblock/Llama3-TenyxChat-70B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tensorblock/Llama3-TenyxChat-70B-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="tensorblock/Llama3-TenyxChat-70B-GGUF") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("tensorblock/Llama3-TenyxChat-70B-GGUF", dtype="auto") - llama-cpp-python
How to use tensorblock/Llama3-TenyxChat-70B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="tensorblock/Llama3-TenyxChat-70B-GGUF", filename="Llama3-TenyxChat-70B-Q2_K.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 tensorblock/Llama3-TenyxChat-70B-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf tensorblock/Llama3-TenyxChat-70B-GGUF:Q2_K # Run inference directly in the terminal: llama-cli -hf tensorblock/Llama3-TenyxChat-70B-GGUF:Q2_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf tensorblock/Llama3-TenyxChat-70B-GGUF:Q2_K # Run inference directly in the terminal: llama-cli -hf tensorblock/Llama3-TenyxChat-70B-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/Llama3-TenyxChat-70B-GGUF:Q2_K # Run inference directly in the terminal: ./llama-cli -hf tensorblock/Llama3-TenyxChat-70B-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/Llama3-TenyxChat-70B-GGUF:Q2_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf tensorblock/Llama3-TenyxChat-70B-GGUF:Q2_K
Use Docker
docker model run hf.co/tensorblock/Llama3-TenyxChat-70B-GGUF:Q2_K
- LM Studio
- Jan
- vLLM
How to use tensorblock/Llama3-TenyxChat-70B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tensorblock/Llama3-TenyxChat-70B-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": "tensorblock/Llama3-TenyxChat-70B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/tensorblock/Llama3-TenyxChat-70B-GGUF:Q2_K
- SGLang
How to use tensorblock/Llama3-TenyxChat-70B-GGUF with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "tensorblock/Llama3-TenyxChat-70B-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tensorblock/Llama3-TenyxChat-70B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "tensorblock/Llama3-TenyxChat-70B-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tensorblock/Llama3-TenyxChat-70B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use tensorblock/Llama3-TenyxChat-70B-GGUF with Ollama:
ollama run hf.co/tensorblock/Llama3-TenyxChat-70B-GGUF:Q2_K
- Unsloth Studio
How to use tensorblock/Llama3-TenyxChat-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 tensorblock/Llama3-TenyxChat-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 tensorblock/Llama3-TenyxChat-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 tensorblock/Llama3-TenyxChat-70B-GGUF to start chatting
- Docker Model Runner
How to use tensorblock/Llama3-TenyxChat-70B-GGUF with Docker Model Runner:
docker model run hf.co/tensorblock/Llama3-TenyxChat-70B-GGUF:Q2_K
- Lemonade
How to use tensorblock/Llama3-TenyxChat-70B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tensorblock/Llama3-TenyxChat-70B-GGUF:Q2_K
Run and chat with the model
lemonade run user.Llama3-TenyxChat-70B-GGUF-Q2_K
List all available models
lemonade list
Upload folder using huggingface_hub
Browse files- .gitattributes +15 -0
- Llama3-TenyxChat-70B-Q2_K.gguf +3 -0
- Llama3-TenyxChat-70B-Q3_K_L.gguf +3 -0
- Llama3-TenyxChat-70B-Q3_K_M.gguf +3 -0
- Llama3-TenyxChat-70B-Q3_K_S.gguf +3 -0
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- Llama3-TenyxChat-70B-Q4_K_M.gguf +3 -0
- Llama3-TenyxChat-70B-Q4_K_S.gguf +3 -0
- Llama3-TenyxChat-70B-Q5_0.gguf +3 -0
- Llama3-TenyxChat-70B-Q5_K_M.gguf +3 -0
- Llama3-TenyxChat-70B-Q5_K_S.gguf +3 -0
- Llama3-TenyxChat-70B-Q6_K/Llama3-TenyxChat-70B-Q6_K-00001-of-00002.gguf +3 -0
- Llama3-TenyxChat-70B-Q6_K/Llama3-TenyxChat-70B-Q6_K-00002-of-00002.gguf +3 -0
- Llama3-TenyxChat-70B-Q8_0/Llama3-TenyxChat-70B-Q8_0-00001-of-00003.gguf +3 -0
- Llama3-TenyxChat-70B-Q8_0/Llama3-TenyxChat-70B-Q8_0-00002-of-00003.gguf +3 -0
- Llama3-TenyxChat-70B-Q8_0/Llama3-TenyxChat-70B-Q8_0-00003-of-00003.gguf +3 -0
- README.md +90 -0
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---
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license: llama3
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datasets:
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- HuggingFaceH4/ultrafeedback_binarized
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language:
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- en
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library_name: transformers
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tags:
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- tenyx-fine-tuning
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- dpo
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- tenyxchat
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- llama3
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- TensorBlock
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- GGUF
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pipeline_tag: text-generation
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base_model: tenyx/Llama3-TenyxChat-70B
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---
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<div style="width: auto; margin-left: auto; margin-right: auto">
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<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
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</div>
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<div style="display: flex; justify-content: space-between; width: 100%;">
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<div style="display: flex; flex-direction: column; align-items: flex-start;">
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<p style="margin-top: 0.5em; margin-bottom: 0em;">
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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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</p>
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</div>
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</div>
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## tenyx/Llama3-TenyxChat-70B - GGUF
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This repo contains GGUF format model files for [tenyx/Llama3-TenyxChat-70B](https://huggingface.co/tenyx/Llama3-TenyxChat-70B).
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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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<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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Run them on the TensorBlock client using your local machine ↗
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</a>
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</div>
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## Prompt template
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```
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<|begin_of_text|><|start_header_id|>system<|end_header_id|>
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+
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{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
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+
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{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
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```
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## Model file specification
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| Filename | Quant type | File Size | Description |
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| -------- | ---------- | --------- | ----------- |
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| [Llama3-TenyxChat-70B-Q2_K.gguf](https://huggingface.co/tensorblock/Llama3-TenyxChat-70B-GGUF/blob/main/Llama3-TenyxChat-70B-Q2_K.gguf) | Q2_K | 26.375 GB | smallest, significant quality loss - not recommended for most purposes |
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| [Llama3-TenyxChat-70B-Q3_K_S.gguf](https://huggingface.co/tensorblock/Llama3-TenyxChat-70B-GGUF/blob/main/Llama3-TenyxChat-70B-Q3_K_S.gguf) | Q3_K_S | 30.912 GB | very small, high quality loss |
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| [Llama3-TenyxChat-70B-Q3_K_M.gguf](https://huggingface.co/tensorblock/Llama3-TenyxChat-70B-GGUF/blob/main/Llama3-TenyxChat-70B-Q3_K_M.gguf) | Q3_K_M | 34.267 GB | very small, high quality loss |
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| [Llama3-TenyxChat-70B-Q3_K_L.gguf](https://huggingface.co/tensorblock/Llama3-TenyxChat-70B-GGUF/blob/main/Llama3-TenyxChat-70B-Q3_K_L.gguf) | Q3_K_L | 37.141 GB | small, substantial quality loss |
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| [Llama3-TenyxChat-70B-Q4_0.gguf](https://huggingface.co/tensorblock/Llama3-TenyxChat-70B-GGUF/blob/main/Llama3-TenyxChat-70B-Q4_0.gguf) | Q4_0 | 39.970 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
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| [Llama3-TenyxChat-70B-Q4_K_S.gguf](https://huggingface.co/tensorblock/Llama3-TenyxChat-70B-GGUF/blob/main/Llama3-TenyxChat-70B-Q4_K_S.gguf) | Q4_K_S | 40.347 GB | small, greater quality loss |
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| [Llama3-TenyxChat-70B-Q4_K_M.gguf](https://huggingface.co/tensorblock/Llama3-TenyxChat-70B-GGUF/blob/main/Llama3-TenyxChat-70B-Q4_K_M.gguf) | Q4_K_M | 42.520 GB | medium, balanced quality - recommended |
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| [Llama3-TenyxChat-70B-Q5_0.gguf](https://huggingface.co/tensorblock/Llama3-TenyxChat-70B-GGUF/blob/main/Llama3-TenyxChat-70B-Q5_0.gguf) | Q5_0 | 48.657 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
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| [Llama3-TenyxChat-70B-Q5_K_S.gguf](https://huggingface.co/tensorblock/Llama3-TenyxChat-70B-GGUF/blob/main/Llama3-TenyxChat-70B-Q5_K_S.gguf) | Q5_K_S | 48.657 GB | large, low quality loss - recommended |
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| [Llama3-TenyxChat-70B-Q5_K_M.gguf](https://huggingface.co/tensorblock/Llama3-TenyxChat-70B-GGUF/blob/main/Llama3-TenyxChat-70B-Q5_K_M.gguf) | Q5_K_M | 49.950 GB | large, very low quality loss - recommended |
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| [Llama3-TenyxChat-70B-Q8_0](https://huggingface.co/tensorblock/Llama3-TenyxChat-70B-GGUF/blob/main/Llama3-TenyxChat-70B-Q8_0) | Q6_K | 74.975 GB | very large, extremely low quality loss |
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| [Llama3-TenyxChat-70B-Q6_K](https://huggingface.co/tensorblock/Llama3-TenyxChat-70B-GGUF/blob/main/Llama3-TenyxChat-70B-Q6_K) | Q8_0 | 57.888 GB | very large, extremely low quality loss - not recommended |
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## Downloading instruction
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### Command line
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Firstly, install Huggingface Client
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```shell
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pip install -U "huggingface_hub[cli]"
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```
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Then, downoad the individual model file the a local directory
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```shell
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huggingface-cli download tensorblock/Llama3-TenyxChat-70B-GGUF --include "Llama3-TenyxChat-70B-Q2_K.gguf" --local-dir MY_LOCAL_DIR
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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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```shell
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huggingface-cli download tensorblock/Llama3-TenyxChat-70B-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
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```
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