Text Generation
GGUF
quantized
GGUF
imatrix
quantization
imat
static
16bit
8bit
6bit
5bit
4bit
3bit
2bit
1bit
conversational
Instructions to use legraphista/Meta-Llama-3-70B-Instruct-abliterated-v3.5-IMat-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use legraphista/Meta-Llama-3-70B-Instruct-abliterated-v3.5-IMat-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="legraphista/Meta-Llama-3-70B-Instruct-abliterated-v3.5-IMat-GGUF", filename="Meta-Llama-3-70B-Instruct-abliterated-v3.5.BF16/Meta-Llama-3-70B-Instruct-abliterated-v3.5.BF16-00001-of-00006.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use legraphista/Meta-Llama-3-70B-Instruct-abliterated-v3.5-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/Meta-Llama-3-70B-Instruct-abliterated-v3.5-IMat-GGUF:Q4_K_S # Run inference directly in the terminal: llama-cli -hf legraphista/Meta-Llama-3-70B-Instruct-abliterated-v3.5-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/Meta-Llama-3-70B-Instruct-abliterated-v3.5-IMat-GGUF:Q4_K_S # Run inference directly in the terminal: llama-cli -hf legraphista/Meta-Llama-3-70B-Instruct-abliterated-v3.5-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/Meta-Llama-3-70B-Instruct-abliterated-v3.5-IMat-GGUF:Q4_K_S # Run inference directly in the terminal: ./llama-cli -hf legraphista/Meta-Llama-3-70B-Instruct-abliterated-v3.5-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/Meta-Llama-3-70B-Instruct-abliterated-v3.5-IMat-GGUF:Q4_K_S # Run inference directly in the terminal: ./build/bin/llama-cli -hf legraphista/Meta-Llama-3-70B-Instruct-abliterated-v3.5-IMat-GGUF:Q4_K_S
Use Docker
docker model run hf.co/legraphista/Meta-Llama-3-70B-Instruct-abliterated-v3.5-IMat-GGUF:Q4_K_S
- LM Studio
- Jan
- vLLM
How to use legraphista/Meta-Llama-3-70B-Instruct-abliterated-v3.5-IMat-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "legraphista/Meta-Llama-3-70B-Instruct-abliterated-v3.5-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/Meta-Llama-3-70B-Instruct-abliterated-v3.5-IMat-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/legraphista/Meta-Llama-3-70B-Instruct-abliterated-v3.5-IMat-GGUF:Q4_K_S
- Ollama
How to use legraphista/Meta-Llama-3-70B-Instruct-abliterated-v3.5-IMat-GGUF with Ollama:
ollama run hf.co/legraphista/Meta-Llama-3-70B-Instruct-abliterated-v3.5-IMat-GGUF:Q4_K_S
- Unsloth Studio new
How to use legraphista/Meta-Llama-3-70B-Instruct-abliterated-v3.5-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/Meta-Llama-3-70B-Instruct-abliterated-v3.5-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/Meta-Llama-3-70B-Instruct-abliterated-v3.5-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/Meta-Llama-3-70B-Instruct-abliterated-v3.5-IMat-GGUF to start chatting
- Docker Model Runner
How to use legraphista/Meta-Llama-3-70B-Instruct-abliterated-v3.5-IMat-GGUF with Docker Model Runner:
docker model run hf.co/legraphista/Meta-Llama-3-70B-Instruct-abliterated-v3.5-IMat-GGUF:Q4_K_S
- Lemonade
How to use legraphista/Meta-Llama-3-70B-Instruct-abliterated-v3.5-IMat-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull legraphista/Meta-Llama-3-70B-Instruct-abliterated-v3.5-IMat-GGUF:Q4_K_S
Run and chat with the model
lemonade run user.Meta-Llama-3-70B-Instruct-abliterated-v3.5-IMat-GGUF-Q4_K_S
List all available models
lemonade list
| main: build = 3058 (30e238b2) | |
| main: built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu | |
| main: seed = 1717189635 | |
| llama_model_loader: loaded meta data with 25 key-value pairs and 723 tensors from Meta-Llama-3-70B-Instruct-abliterated-v3.5.Q8_0.gguf (version GGUF V3 (latest)) | |
| llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output. | |
| llama_model_loader: - kv 0: general.architecture str = llama | |
| llama_model_loader: - kv 1: general.name str = Meta-Llama-3-70B-Instruct-abliterated... | |
| llama_model_loader: - kv 2: llama.block_count u32 = 80 | |
| llama_model_loader: - kv 3: llama.context_length u32 = 8192 | |
| llama_model_loader: - kv 4: llama.embedding_length u32 = 8192 | |
| llama_model_loader: - kv 5: llama.feed_forward_length u32 = 28672 | |
| llama_model_loader: - kv 6: llama.attention.head_count u32 = 64 | |
| llama_model_loader: - kv 7: llama.attention.head_count_kv u32 = 8 | |
| llama_model_loader: - kv 8: llama.rope.freq_base f32 = 500000.000000 | |
| llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000010 | |
| llama_model_loader: - kv 10: general.file_type u32 = 7 | |
| llama_model_loader: - kv 11: llama.vocab_size u32 = 128256 | |
| llama_model_loader: - kv 12: llama.rope.dimension_count u32 = 128 | |
| llama_model_loader: - kv 13: tokenizer.ggml.model str = gpt2 | |
| llama_model_loader: - kv 14: tokenizer.ggml.pre str = llama-bpe | |
| llama_model_loader: - kv 15: tokenizer.ggml.tokens arr[str,128256] = | |