Instructions to use appvoid/palmer-instruct-test-14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use appvoid/palmer-instruct-test-14 with Transformers:

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

pipe = pipeline("text-generation", model="appvoid/palmer-instruct-test-14")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("appvoid/palmer-instruct-test-14")
model = AutoModelForCausalLM.from_pretrained("appvoid/palmer-instruct-test-14")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use appvoid/palmer-instruct-test-14 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "appvoid/palmer-instruct-test-14"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "appvoid/palmer-instruct-test-14",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/appvoid/palmer-instruct-test-14

SGLang

How to use appvoid/palmer-instruct-test-14 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 "appvoid/palmer-instruct-test-14" \
    --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": "appvoid/palmer-instruct-test-14",
		"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 "appvoid/palmer-instruct-test-14" \
        --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": "appvoid/palmer-instruct-test-14",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use appvoid/palmer-instruct-test-14 with Docker Model Runner:
```
docker model run hf.co/appvoid/palmer-instruct-test-14
```

palmer-instruct-test-14 / README.md

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metadata

base_model:
  - appvoid/palmer-003
  - l3utterfly/tinyllama-1.1b-layla-v4
  - TinyLlama/TinyLlama-1.1B-intermediate-step-1195k-token-2.5T
  - vihangd/DopeyTinyLlama-1.1B-v1
library_name: transformers
tags:
  - mergekit
  - merge

merge

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

Merge Details

Merge Method

This model was merged using the TIES merge method using TinyLlama/TinyLlama-1.1B-intermediate-step-1195k-token-2.5T as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: TinyLlama/TinyLlama-1.1B-intermediate-step-1195k-token-2.5T
    #no parameters necessary for base model
  - model: vihangd/DopeyTinyLlama-1.1B-v1
    parameters:
      density: 0.10
      weight: 0.60
  - model: l3utterfly/tinyllama-1.1b-layla-v4
    parameters:
      density: 0.39
      weight: 0.50
  - model: appvoid/palmer-003
    parameters:
      density: 0.60
      weight: 0.40

merge_method: ties
base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1195k-token-2.5T
parameters:
  normalize: true
  int8_mask: true
dtype: float16