Instructions to use Fischerboot/keksmall2borsmth2222 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Fischerboot/keksmall2borsmth2222 with Transformers:

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

pipe = pipeline("text-generation", model="Fischerboot/keksmall2borsmth2222")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Fischerboot/keksmall2borsmth2222")
model = AutoModelForMultimodalLM.from_pretrained("Fischerboot/keksmall2borsmth2222")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Fischerboot/keksmall2borsmth2222 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Fischerboot/keksmall2borsmth2222"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Fischerboot/keksmall2borsmth2222",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Fischerboot/keksmall2borsmth2222

SGLang

How to use Fischerboot/keksmall2borsmth2222 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 "Fischerboot/keksmall2borsmth2222" \
    --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": "Fischerboot/keksmall2borsmth2222",
		"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 "Fischerboot/keksmall2borsmth2222" \
        --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": "Fischerboot/keksmall2borsmth2222",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Fischerboot/keksmall2borsmth2222 with Docker Model Runner:
```
docker model run hf.co/Fischerboot/keksmall2borsmth2222
```

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.

Models Merged

The following models were included in the merge:

TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T

Configuration

The following YAML configuration was used to produce this model:

dtype: bfloat16
merge_method: passthrough
slices:
- sources:
  - layer_range: [0, 16] # angepasst von [0, 24] auf [0, 16]
    model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
- sources:
  - layer_range: [5, 16] # angepasst von [8, 24] auf [5, 16]
    model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
    parameters:
      scale:
      - filter: o_proj
        value: 0.0
      - filter: down_proj
        value: 0.0
      - value: 1.0
- sources:
  - layer_range: [5, 16] # angepasst von [8, 24] auf [5, 16]
    model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
    parameters:
      scale:
      - filter: o_proj
        value: 0.0
      - filter: down_proj
        value: 0.0
      - value: 1.0
- sources:
  - layer_range: [16, 22] # angepasst von [24, 32] auf [16, 22]
    model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T

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Safetensors

Model size

2B params

Tensor type

BF16

Model tree for Fischerboot/keksmall2borsmth2222

Base model

TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T

Finetuned

(105)

this model