teknium/OpenHermes-2.5
Viewer • Updated • 1M • 21.5k • 891
How to use QuantFactory/AI-Sweden-Llama-3-8B-Instruct-GGUF with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf QuantFactory/AI-Sweden-Llama-3-8B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/AI-Sweden-Llama-3-8B-Instruct-GGUF:Q4_K_M
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf QuantFactory/AI-Sweden-Llama-3-8B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/AI-Sweden-Llama-3-8B-Instruct-GGUF:Q4_K_M
# 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 QuantFactory/AI-Sweden-Llama-3-8B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf QuantFactory/AI-Sweden-Llama-3-8B-Instruct-GGUF:Q4_K_M
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 QuantFactory/AI-Sweden-Llama-3-8B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuantFactory/AI-Sweden-Llama-3-8B-Instruct-GGUF:Q4_K_M
docker model run hf.co/QuantFactory/AI-Sweden-Llama-3-8B-Instruct-GGUF:Q4_K_M
How to use QuantFactory/AI-Sweden-Llama-3-8B-Instruct-GGUF with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/AI-Sweden-Llama-3-8B-Instruct-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": "QuantFactory/AI-Sweden-Llama-3-8B-Instruct-GGUF",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'docker model run hf.co/QuantFactory/AI-Sweden-Llama-3-8B-Instruct-GGUF:Q4_K_M
How to use QuantFactory/AI-Sweden-Llama-3-8B-Instruct-GGUF with Ollama:
ollama run hf.co/QuantFactory/AI-Sweden-Llama-3-8B-Instruct-GGUF:Q4_K_M
How to use QuantFactory/AI-Sweden-Llama-3-8B-Instruct-GGUF with Unsloth Studio:
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 QuantFactory/AI-Sweden-Llama-3-8B-Instruct-GGUF to start chatting
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 QuantFactory/AI-Sweden-Llama-3-8B-Instruct-GGUF to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for QuantFactory/AI-Sweden-Llama-3-8B-Instruct-GGUF to start chatting
How to use QuantFactory/AI-Sweden-Llama-3-8B-Instruct-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/AI-Sweden-Llama-3-8B-Instruct-GGUF:Q4_K_M
How to use QuantFactory/AI-Sweden-Llama-3-8B-Instruct-GGUF with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantFactory/AI-Sweden-Llama-3-8B-Instruct-GGUF:Q4_K_M
lemonade run user.AI-Sweden-Llama-3-8B-Instruct-GGUF-Q4_K_M
lemonade list
This is quantized version of AI-Sweden-Models/Llama-3-8B-instruct created suing llama.cpp
The training was perfomed on the LUMI supercomputer within the DeployAI EU project. Based of the base model AI-Sweden-Models/Llama-3-8B.
A data recipe by: 42 Labs

import transformers
import torch
model_id = "AI-Sweden-Models/Llama-3-8B-instruct"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
messages = [
{"role": "system", "content": "Du är en hjälpsam assistant som svarar klokt och vänligt."},
{"role": "user", "content": "Hur gör man pannkakor? Och vad behöver man handla? Undrar också vad 5+6 är.."},
]
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = pipeline(
messages,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
print(outputs[0]["generated_text"][-1])
>>> "För att göra pannkakor behöver du följande ingredienser:
- 1 kopp vetemjöl
- 1 tesked bakpulver
- 1/4 tesked salt
- 1 kopp mjölk
- 1 stort ägg
- 2 matskedar smält smör eller olja
För att börja, blanda vetemjölet, bakpulvret och saltet i en bunke. I en annan skål, vispa ihop mjölken, ägget och smöret eller oljan.
Tillsätt de våta ingredienserna till de torra ingredienserna och blanda tills det är väl blandat.
Låt smeten vila i cirka 10 minuter.
För att göra pannkakorna, värm en non-stick-panna eller stekpanna över medelvärme.
När den är varm, häll smeten på pannan och grädda tills kanterna börjar torka ut och toppen är fast.
Vänd pannkakan med en stekspade och grädda den andra sidan tills den är gyllenbrun.
Upprepa med resten av smeten.
När det gäller 5+6 är svaret 11."
2-bit
3-bit
4-bit
5-bit
6-bit
8-bit
Base model
meta-llama/Meta-Llama-3-8B