Instructions to use RichardErkhov/fblgit_-_UNA-POLAR-10.7B-InstructMath-v2-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use RichardErkhov/fblgit_-_UNA-POLAR-10.7B-InstructMath-v2-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 RichardErkhov/fblgit_-_UNA-POLAR-10.7B-InstructMath-v2-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf RichardErkhov/fblgit_-_UNA-POLAR-10.7B-InstructMath-v2-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 RichardErkhov/fblgit_-_UNA-POLAR-10.7B-InstructMath-v2-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf RichardErkhov/fblgit_-_UNA-POLAR-10.7B-InstructMath-v2-gguf:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf RichardErkhov/fblgit_-_UNA-POLAR-10.7B-InstructMath-v2-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf RichardErkhov/fblgit_-_UNA-POLAR-10.7B-InstructMath-v2-gguf:Q4_K_M
Use Docker
docker model run hf.co/RichardErkhov/fblgit_-_UNA-POLAR-10.7B-InstructMath-v2-gguf:Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use RichardErkhov/fblgit_-_UNA-POLAR-10.7B-InstructMath-v2-gguf with Ollama:
ollama run hf.co/RichardErkhov/fblgit_-_UNA-POLAR-10.7B-InstructMath-v2-gguf:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use RichardErkhov/fblgit_-_UNA-POLAR-10.7B-InstructMath-v2-gguf with Docker Model Runner:
docker model run hf.co/RichardErkhov/fblgit_-_UNA-POLAR-10.7B-InstructMath-v2-gguf:Q4_K_M
- Lemonade
How to use RichardErkhov/fblgit_-_UNA-POLAR-10.7B-InstructMath-v2-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull RichardErkhov/fblgit_-_UNA-POLAR-10.7B-InstructMath-v2-gguf:Q4_K_M
Run and chat with the model
lemonade run user.fblgit_-_UNA-POLAR-10.7B-InstructMath-v2-gguf-Q4_K_M
List all available models
lemonade list
- Atomic Chat
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Quantization made by Richard Erkhov.
UNA-POLAR-10.7B-InstructMath-v2 - GGUF
- Model creator: https://huggingface.co/fblgit/
- Original model: https://huggingface.co/fblgit/UNA-POLAR-10.7B-InstructMath-v2/
Original model description:
language: - en license: cc-by-nc-nd-4.0 tags: - UNA - SOLAR - MathPILE datasets: - GAIR/MathPile model-index: - name: UNA-POLAR-10.7B-InstructMath-v2 results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 70.73 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-POLAR-10.7B-InstructMath-v2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 88.2 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-POLAR-10.7B-InstructMath-v2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 66.03 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-POLAR-10.7B-InstructMath-v2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 71.73 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-POLAR-10.7B-InstructMath-v2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 82.95 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-POLAR-10.7B-InstructMath-v2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 64.75 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-POLAR-10.7B-InstructMath-v2 name: Open LLM Leaderboard
UNA-POLAR-10.7B-InstructMath-v2
Model description
Its a UNA version with DPO over MathPILE Books out of the UNA-SOLAR-10.7B-Instruct-1.0
I used MathPILE OUTSTANDING Dataset of great Mathematic material in order to produce this beautiful model :)
Intended uses & limitations
If your model has inside UNA technology, cite.
Training and evaluation data
UNA-DPO over Attention and MLP's
Framework versions
- PEFT 0.7.1
- Transformers 4.36.2-UNA
- Pytorch 2.1.2+cu121
- Datasets 2.16.0
- Tokenizers 0.15.
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 74.07 |
| AI2 Reasoning Challenge (25-Shot) | 70.73 |
| HellaSwag (10-Shot) | 88.20 |
| MMLU (5-Shot) | 66.03 |
| TruthfulQA (0-shot) | 71.73 |
| Winogrande (5-shot) | 82.95 |
| GSM8k (5-shot) | 64.75 |
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