Instructions to use mergekit-community/llasa-3b-upscaled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mergekit-community/llasa-3b-upscaled with Transformers:

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

pipe = pipeline("text-generation", model="mergekit-community/llasa-3b-upscaled")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mergekit-community/llasa-3b-upscaled")
model = AutoModelForCausalLM.from_pretrained("mergekit-community/llasa-3b-upscaled")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use mergekit-community/llasa-3b-upscaled with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mergekit-community/llasa-3b-upscaled"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mergekit-community/llasa-3b-upscaled",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mergekit-community/llasa-3b-upscaled

SGLang

How to use mergekit-community/llasa-3b-upscaled 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 "mergekit-community/llasa-3b-upscaled" \
    --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": "mergekit-community/llasa-3b-upscaled",
		"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 "mergekit-community/llasa-3b-upscaled" \
        --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": "mergekit-community/llasa-3b-upscaled",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mergekit-community/llasa-3b-upscaled with Docker Model Runner:
```
docker model run hf.co/mergekit-community/llasa-3b-upscaled
```

Configuration Parsing Warning:Config file tokenizer_config.json cannot be fetched (too big)

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:

srinivasbilla/llasa-3b

Configuration

The following YAML configuration was used to produce this model:

dtype: bfloat16
merge_method: passthrough
slices:
- sources:
  - layer_range: [0, 21]
    model: srinivasbilla/llasa-3b
- sources:
  - layer_range: [11, 22]
    model: srinivasbilla/llasa-3b
    parameters:
      scale:
      - filter: o_proj
        value: 0.0
      - filter: down_proj
        value: 0.0
      - value: 1.0
- sources:
  - layer_range: [11, 22]
    model: srinivasbilla/llasa-3b
    parameters:
      scale:
      - filter: o_proj
        value: 0.0
      - filter: down_proj
        value: 0.0
      - value: 1.0
- sources:
  - layer_range: [22, 28]
    model: srinivasbilla/llasa-3b

Downloads last month: -

Safetensors

Model size

6B params

Tensor type

BF16

Model tree for mergekit-community/llasa-3b-upscaled

Base model

srinivasbilla/llasa-3b

Finetuned

(1)

this model