Instructions to use mpasila/Finnish-Alpacazord-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mpasila/Finnish-Alpacazord-7B with Transformers:

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

pipe = pipeline("text-generation", model="mpasila/Finnish-Alpacazord-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mpasila/Finnish-Alpacazord-7B")
model = AutoModelForCausalLM.from_pretrained("mpasila/Finnish-Alpacazord-7B")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mpasila/Finnish-Alpacazord-7B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/mpasila/Finnish-Alpacazord-7B

SGLang

How to use mpasila/Finnish-Alpacazord-7B 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 "mpasila/Finnish-Alpacazord-7B" \
    --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": "mpasila/Finnish-Alpacazord-7B",
		"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 "mpasila/Finnish-Alpacazord-7B" \
        --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": "mpasila/Finnish-Alpacazord-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mpasila/Finnish-Alpacazord-7B with Docker Model Runner:
```
docker model run hf.co/mpasila/Finnish-Alpacazord-7B
```

Finnish-Alpacazord-7B

This is a merge of pre-trained language models created using mergekit.

This is just a test to see if I can improve my models by merging the Finnish data based model with the slightly higher performing English data based model.

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

slices:
  - sources:
      - model: mpasila/Finnish-Viking-Alpaca-V1-7B
        layer_range: [0, 32]
      - model: mpasila/Alpacazord-Viking-7B
        layer_range: [0, 32]
# or, the equivalent models: syntax:
# models:
#   - model: psmathur/orca_mini_v3_13b
#   - model: garage-bAInd/Platypus2-13B
merge_method: slerp
base_model: mpasila/Finnish-Viking-Alpaca-V1-7B
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5 # fallback for rest of tensors
dtype: float16

Downloads last month: 3

Safetensors

Model size

8B params

Tensor type

F16

Model tree for mpasila/Finnish-Alpacazord-7B

mpasila/Alpacazord-Viking-7B

mpasila/Finnish-Viking-Alpaca-V1-7B

Merge model

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Collection including mpasila/Finnish-Alpacazord-7B

Finnish fine-tunes

Collection

All my Finnish fine-tuned models. • 23 items • Updated Jul 19, 2024 • 2