Instructions to use Mabeck/Heidrun-Mistral-7B-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Mabeck/Heidrun-Mistral-7B-chat with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Mabeck/Heidrun-Mistral-7B-chat")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Mabeck/Heidrun-Mistral-7B-chat")
model = AutoModelForCausalLM.from_pretrained("Mabeck/Heidrun-Mistral-7B-chat")

llama-cpp-python

How to use Mabeck/Heidrun-Mistral-7B-chat with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Mabeck/Heidrun-Mistral-7B-chat",
	filename="Heidrun-Mistral-7B-chat-Q6_K.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Mabeck/Heidrun-Mistral-7B-chat 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 Mabeck/Heidrun-Mistral-7B-chat:Q6_K
# Run inference directly in the terminal:
llama cli -hf Mabeck/Heidrun-Mistral-7B-chat:Q6_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Mabeck/Heidrun-Mistral-7B-chat:Q6_K
# Run inference directly in the terminal:
llama cli -hf Mabeck/Heidrun-Mistral-7B-chat:Q6_K

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 Mabeck/Heidrun-Mistral-7B-chat:Q6_K
# Run inference directly in the terminal:
./llama-cli -hf Mabeck/Heidrun-Mistral-7B-chat:Q6_K

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 Mabeck/Heidrun-Mistral-7B-chat:Q6_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Mabeck/Heidrun-Mistral-7B-chat:Q6_K

Use Docker

docker model run hf.co/Mabeck/Heidrun-Mistral-7B-chat:Q6_K

LM Studio
Jan

vLLM

How to use Mabeck/Heidrun-Mistral-7B-chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Mabeck/Heidrun-Mistral-7B-chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mabeck/Heidrun-Mistral-7B-chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Mabeck/Heidrun-Mistral-7B-chat:Q6_K

SGLang

How to use Mabeck/Heidrun-Mistral-7B-chat with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Mabeck/Heidrun-Mistral-7B-chat" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mabeck/Heidrun-Mistral-7B-chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Mabeck/Heidrun-Mistral-7B-chat" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mabeck/Heidrun-Mistral-7B-chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use Mabeck/Heidrun-Mistral-7B-chat with Ollama:
```
ollama run hf.co/Mabeck/Heidrun-Mistral-7B-chat:Q6_K
```

Unsloth Studio

How to use Mabeck/Heidrun-Mistral-7B-chat 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 Mabeck/Heidrun-Mistral-7B-chat 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 Mabeck/Heidrun-Mistral-7B-chat to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Mabeck/Heidrun-Mistral-7B-chat to start chatting

Atomic Chat new
Docker Model Runner
How to use Mabeck/Heidrun-Mistral-7B-chat with Docker Model Runner:
```
docker model run hf.co/Mabeck/Heidrun-Mistral-7B-chat:Q6_K
```

Lemonade

How to use Mabeck/Heidrun-Mistral-7B-chat with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Mabeck/Heidrun-Mistral-7B-chat:Q6_K

Run and chat with the model

lemonade run user.Heidrun-Mistral-7B-chat-Q6_K

List all available models

lemonade list

Heidrun-Mistral-7B-chat / README.md

saattrupdan

Add `text-generation` as task tag

d1b0b92 verified over 2 years ago

preview code

Raw

History Blame

3.8 kB

	---
	language:
	- en
	- da
	tags:
	- text-generation-inference
	- transformers
	- unsloth
	- mistral
	- trl
	base_model: Mabeck/Heidrun-Mistral-7B-base
	datasets:
	- Mabeck/danish-OpenHermes
	- kobprof/skolegpt-instruct
	license: mit
	pipeline_tag: text-generation
	---


	<img src="https://huggingface.co/Mabeck/Heidrun-Mistral-7B-chat/resolve/main/heidrun.jpeg" alt="Heidrun Logo" width="400">

	# Model description
	Heidrun-Mistral-7B-chat is a chat-model based on [Heidrun-Mistral-7B-base](https://huggingface.co/Mabeck/Heidrun-Mistral-7B-base), finetuned on [danish-OpenHermes](https://huggingface.co/datasets/Mabeck/danish-OpenHermes) and [skoleGPT](https://huggingface.co/datasets/kobprof/skolegpt-instruct) for a instruction/chat format.

	It is a new SOTA Danish open-source LLM and shows very strong performance in logic and reasoning tasks.

	Heidrun-7B ranks 1st among Danish open-sourced LLMs based on the [ScandEval](https://scandeval.com/mainland-scandinavian-nlg/) benchmark and shares 1st place on all Scandinavian languages.

	# Benchmarks

	The following benchmarks have been tested using [ScandEval](https://github.com/ScandEval/ScandEval). Rankings don't include merged models and GPT4 and GPT3.5 ranks 1 and 2:

	- MMLU-da: 35.66%+-0.85%/51.68+-0.63%, ranks 3rd
	- DANSK: 50.80% ± 2.33% / 34.04% ± 1.76%, ranks 3rd=
	- Hellaswag-da: 29.18% ± 0.99%/ 46.64% ± 0.76%, ranks 4th

	# Datasets
	This model is trained on Danish instruction datasets [danish-OpenHermes](Mabeck/danish-OpenHermes) and [skoleGPT](https://huggingface.co/datasets/kobprof/skolegpt-instruct), which have not been safeguarded or alligned.

	Most of the data has been machine-translated and may contain incorrect responses.

	# Samples

	This model uses the ChatML format. Using other formats may degrade the model's performance.

	Samples from the model using ChatML format:
	```
	"<s> <im_start>system
	Du er en AI-hjælper. Du får en opgave fra en bruger hvor du kort og præcist skal forklare svaret.
	<im_end>
	<im_start>user
	Hvilke 3 populære retter findes der i dansk madlavning?
	<im_end>
	<im_start>assistant
	1. Smørrebrød: Smørrebrød er en populær dansk ret, der består af brød med forskellige toppings som røget laks, rejer, ost eller andre ingredienser.

	2. Frikadeller: Frikadeller er en traditionel dansk ret lavet af kødboller af kalvekød, svinekød eller lam, krydret med krydderurter og kogt i smør eller olie.

	3. Rød kål: Rød kål er en populær side ret i dansk madlavning.
	<im_end></s>"
	```

	```
	"<s> <im_start>system
	Du er en AI-hjælper. Du får en opgave fra en bruger hvor du kort og præcist skal forklare svaret.
	<im_end>
	<im_start>user
	Hvilke 3 områder skal fokuseres på for at effektivt tackle klimaforandringer?
	<im_end>
	<im_start>assistant
	1. Energiproduktion: Fokus på vedvarende energikilder som sol, vind og vandkraft for at reducere afhængigheden af fossile brændstoffer og minimere drivhusgasemissioner.

	2. Transport: Fremme bæredygtige transportformer som offentlig transport, cykling og elektriske køretøjer for at reducere luftforurening og afhængigheden af fossile brændstoffer.

	3. Skovbevarelse og affaldshåndtering: Fremme skovbevarelse og bæredygtig skovdrift for at fange kuldioxid og reducere drivhusgasemissioner, samt fremme genbrug og reducere affaldsproduktion for at mindske mængden af drivhusgasemissioner fra affald.
	<im_end></s>"
	```

	# Uploaded model

	- Developed by: Mabeck
	- Finetuned from model : Mabeck/Heidrun-Mistral-7B-base

	This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

	[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)