Instructions to use ClaudioItaly/Fimbulvetr-40 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ClaudioItaly/Fimbulvetr-40 with Transformers:

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

pipe = pipeline("text-generation", model="ClaudioItaly/Fimbulvetr-40")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ClaudioItaly/Fimbulvetr-40")
model = AutoModelForCausalLM.from_pretrained("ClaudioItaly/Fimbulvetr-40")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ClaudioItaly/Fimbulvetr-40 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ClaudioItaly/Fimbulvetr-40"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ClaudioItaly/Fimbulvetr-40",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ClaudioItaly/Fimbulvetr-40

SGLang

How to use ClaudioItaly/Fimbulvetr-40 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 "ClaudioItaly/Fimbulvetr-40" \
    --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": "ClaudioItaly/Fimbulvetr-40",
		"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 "ClaudioItaly/Fimbulvetr-40" \
        --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": "ClaudioItaly/Fimbulvetr-40",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ClaudioItaly/Fimbulvetr-40 with Docker Model Runner:
```
docker model run hf.co/ClaudioItaly/Fimbulvetr-40
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Changing the value of kv_count from 34 to 40 indicates an increase in the number of key-value pairs in the model. These key-value pairs are mainly used to represent attention information within neural networks, particularly in Transformer-type models such as LLaMA.

merge

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

Merge Details

Merge Method

This model was merged using the passthrough merge method using Sao10K/Fimbulvetr-11B-v2 as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

base_model:  Sao10K/Fimbulvetr-11B-v2 
merge_method: passthrough
dtype: float16
parameters:
  normalize: true

slices:
  - sources:
      - model: Sao10K/Fimbulvetr-11B-v2
        layer_range: [0, 48]  # Assumi che il modello abbia 48 layer
    densify:
      - linear
      - "rope:alpha=8192/4096"  # Estende il contesto a 8192

tokens:
  - source: Sao10K/Fimbulvetr-11B-v2
    mode: stretch

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Safetensors

Model size

11B params

Tensor type

F16

Model tree for ClaudioItaly/Fimbulvetr-40

Base model

Sao10K/Fimbulvetr-11B-v2

Finetuned

(10)

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Quantizations

2 models