Instructions to use RichardErkhov/mlfoundations-dev_-_OH_original_wo_glaive_code_assistant-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use RichardErkhov/mlfoundations-dev_-_OH_original_wo_glaive_code_assistant-gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="RichardErkhov/mlfoundations-dev_-_OH_original_wo_glaive_code_assistant-gguf", filename="OH_original_wo_glaive_code_assistant.IQ3_M.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use RichardErkhov/mlfoundations-dev_-_OH_original_wo_glaive_code_assistant-gguf with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf RichardErkhov/mlfoundations-dev_-_OH_original_wo_glaive_code_assistant-gguf:Q4_K_M # Run inference directly in the terminal: llama-cli -hf RichardErkhov/mlfoundations-dev_-_OH_original_wo_glaive_code_assistant-gguf:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf RichardErkhov/mlfoundations-dev_-_OH_original_wo_glaive_code_assistant-gguf:Q4_K_M # Run inference directly in the terminal: llama-cli -hf RichardErkhov/mlfoundations-dev_-_OH_original_wo_glaive_code_assistant-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 RichardErkhov/mlfoundations-dev_-_OH_original_wo_glaive_code_assistant-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf RichardErkhov/mlfoundations-dev_-_OH_original_wo_glaive_code_assistant-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 RichardErkhov/mlfoundations-dev_-_OH_original_wo_glaive_code_assistant-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf RichardErkhov/mlfoundations-dev_-_OH_original_wo_glaive_code_assistant-gguf:Q4_K_M
Use Docker
docker model run hf.co/RichardErkhov/mlfoundations-dev_-_OH_original_wo_glaive_code_assistant-gguf:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use RichardErkhov/mlfoundations-dev_-_OH_original_wo_glaive_code_assistant-gguf with Ollama:
ollama run hf.co/RichardErkhov/mlfoundations-dev_-_OH_original_wo_glaive_code_assistant-gguf:Q4_K_M
- Unsloth Studio new
How to use RichardErkhov/mlfoundations-dev_-_OH_original_wo_glaive_code_assistant-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 RichardErkhov/mlfoundations-dev_-_OH_original_wo_glaive_code_assistant-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 RichardErkhov/mlfoundations-dev_-_OH_original_wo_glaive_code_assistant-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for RichardErkhov/mlfoundations-dev_-_OH_original_wo_glaive_code_assistant-gguf to start chatting
- Docker Model Runner
How to use RichardErkhov/mlfoundations-dev_-_OH_original_wo_glaive_code_assistant-gguf with Docker Model Runner:
docker model run hf.co/RichardErkhov/mlfoundations-dev_-_OH_original_wo_glaive_code_assistant-gguf:Q4_K_M
- Lemonade
How to use RichardErkhov/mlfoundations-dev_-_OH_original_wo_glaive_code_assistant-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull RichardErkhov/mlfoundations-dev_-_OH_original_wo_glaive_code_assistant-gguf:Q4_K_M
Run and chat with the model
lemonade run user.mlfoundations-dev_-_OH_original_wo_glaive_code_assistant-gguf-Q4_K_M
List all available models
lemonade list
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Quantization made by Richard Erkhov.
OH_original_wo_glaive_code_assistant - GGUF
- Model creator: https://huggingface.co/mlfoundations-dev/
- Original model: https://huggingface.co/mlfoundations-dev/OH_original_wo_glaive_code_assistant/
Original model description:
library_name: transformers license: llama3.1 base_model: meta-llama/Llama-3.1-8B tags: - llama-factory - full - generated_from_trainer model-index: - name: OH_original_wo_glaive_code_assistant results: []
OH_original_wo_glaive_code_assistant
This model is a fine-tuned version of meta-llama/Llama-3.1-8B on the mlfoundations-dev/OH_original_wo_glaive_code_assistant dataset. It achieves the following results on the evaluation set:
- Loss: 0.6022
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 16
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.1
- lr_scheduler_warmup_steps: 1738
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.6112 | 0.9981 | 265 | 0.6055 |
| 0.5554 | 2.0 | 531 | 0.5974 |
| 0.5025 | 2.9944 | 795 | 0.6022 |
Framework versions
- Transformers 4.45.2
- Pytorch 2.3.0
- Datasets 2.21.0
- Tokenizers 0.20.1
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