GGUF
English
axolotl
Generated from Trainer
instruct
finetune
chatml
gpt4
synthetic data
science
physics
chemistry
biology
math
llama
llama3
llama-cpp
gguf-my-repo
Eval Results (legacy)
conversational
Instructions to use hus960/Einstein-v6.1-Llama3-8B-Q4_K_M-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 hus960/Einstein-v6.1-Llama3-8B-Q4_K_M-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 hus960/Einstein-v6.1-Llama3-8B-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf hus960/Einstein-v6.1-Llama3-8B-Q4_K_M-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 hus960/Einstein-v6.1-Llama3-8B-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf hus960/Einstein-v6.1-Llama3-8B-Q4_K_M-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 hus960/Einstein-v6.1-Llama3-8B-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf hus960/Einstein-v6.1-Llama3-8B-Q4_K_M-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 hus960/Einstein-v6.1-Llama3-8B-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf hus960/Einstein-v6.1-Llama3-8B-Q4_K_M-GGUF:Q4_K_M
Use Docker
docker model run hf.co/hus960/Einstein-v6.1-Llama3-8B-Q4_K_M-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use hus960/Einstein-v6.1-Llama3-8B-Q4_K_M-GGUF with Ollama:
ollama run hf.co/hus960/Einstein-v6.1-Llama3-8B-Q4_K_M-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use hus960/Einstein-v6.1-Llama3-8B-Q4_K_M-GGUF with Docker Model Runner:
docker model run hf.co/hus960/Einstein-v6.1-Llama3-8B-Q4_K_M-GGUF:Q4_K_M
- Lemonade
How to use hus960/Einstein-v6.1-Llama3-8B-Q4_K_M-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull hus960/Einstein-v6.1-Llama3-8B-Q4_K_M-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Einstein-v6.1-Llama3-8B-Q4_K_M-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
metadata
language:
- en
license: other
tags:
- axolotl
- generated_from_trainer
- instruct
- finetune
- chatml
- gpt4
- synthetic data
- science
- physics
- chemistry
- biology
- math
- llama
- llama3
- llama-cpp
- gguf-my-repo
base_model: meta-llama/Meta-Llama-3-8B
datasets:
- allenai/ai2_arc
- camel-ai/physics
- camel-ai/chemistry
- camel-ai/biology
- camel-ai/math
- metaeval/reclor
- openbookqa
- mandyyyyii/scibench
- derek-thomas/ScienceQA
- TIGER-Lab/ScienceEval
- jondurbin/airoboros-3.2
- LDJnr/Capybara
- Cot-Alpaca-GPT4-From-OpenHermes-2.5
- STEM-AI-mtl/Electrical-engineering
- knowrohit07/saraswati-stem
- sablo/oasst2_curated
- lmsys/lmsys-chat-1m
- TIGER-Lab/MathInstruct
- bigbio/med_qa
- meta-math/MetaMathQA-40K
- openbookqa
- piqa
- metaeval/reclor
- derek-thomas/ScienceQA
- scibench
- sciq
- Open-Orca/SlimOrca
- migtissera/Synthia-v1.3
- TIGER-Lab/ScienceEval
- allenai/WildChat
- microsoft/orca-math-word-problems-200k
- openchat/openchat_sharegpt4_dataset
- teknium/GPTeacher-General-Instruct
- m-a-p/CodeFeedback-Filtered-Instruction
- totally-not-an-llm/EverythingLM-data-V3
- HuggingFaceH4/no_robots
- OpenAssistant/oasst_top1_2023-08-25
- WizardLM/WizardLM_evol_instruct_70k
model-index:
- name: Einstein-v6.1-Llama3-8B
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: 62.46
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v6.1-Llama3-8B
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: 82.41
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v6.1-Llama3-8B
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: 66.19
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v6.1-Llama3-8B
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: 55.1
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v6.1-Llama3-8B
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: 79.32
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v6.1-Llama3-8B
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: 66.11
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v6.1-Llama3-8B
name: Open LLM Leaderboard
hus960/Einstein-v6.1-Llama3-8B-Q4_K_M-GGUF
This model was converted to GGUF format from Weyaxi/Einstein-v6.1-Llama3-8B using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the original model card for more details on the model.
Use with llama.cpp
Install llama.cpp through brew.
brew install ggerganov/ggerganov/llama.cpp
Invoke the llama.cpp server or the CLI.
CLI:
llama-cli --hf-repo hus960/Einstein-v6.1-Llama3-8B-Q4_K_M-GGUF --model einstein-v6.1-llama3-8b.Q4_K_M.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo hus960/Einstein-v6.1-Llama3-8B-Q4_K_M-GGUF --model einstein-v6.1-llama3-8b.Q4_K_M.gguf -c 2048
Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m einstein-v6.1-llama3-8b.Q4_K_M.gguf -n 128