Instructions to use appvoid/palmer-006 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use appvoid/palmer-006 with Transformers:

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

pipe = pipeline("text-generation", model="appvoid/palmer-006")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("appvoid/palmer-006")
model = AutoModelForCausalLM.from_pretrained("appvoid/palmer-006", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use appvoid/palmer-006 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf appvoid/palmer-006
# Run inference directly in the terminal:
llama cli -hf appvoid/palmer-006

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf appvoid/palmer-006
# Run inference directly in the terminal:
llama cli -hf appvoid/palmer-006

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf appvoid/palmer-006
# Run inference directly in the terminal:
./build/bin/llama cli -hf appvoid/palmer-006

Use Docker

docker model run hf.co/appvoid/palmer-006

Llama new
LM Studio
Jan

vLLM

How to use appvoid/palmer-006 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "appvoid/palmer-006"
# 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-006",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/appvoid/palmer-006

SGLang

How to use appvoid/palmer-006 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-006" \
    --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-006",
		"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-006" \
        --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-006",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use appvoid/palmer-006 with Ollama:
```
ollama run hf.co/appvoid/palmer-006
```
Unsloth Desktop
Docker Model Runner
How to use appvoid/palmer-006 with Docker Model Runner:
```
docker model run hf.co/appvoid/palmer-006
```

Lemonade

How to use appvoid/palmer-006 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull appvoid/palmer-006

Run and chat with the model

lemonade run user.palmer-006-{{QUANT_TAG}}

List all available models

lemonade list

Atomic Chat

appvoid commited on Jul 29

Commit

7f35f41

verified ·

1 Parent(s): 9058784

Update README.md

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@@ -447,11 +447,12 @@ table {
 We celebrate three years of experiments with the release of `palmer-006`, our flagship and extremely small language model for local experimentation, edge hardware, small SBCs, and low-power devices. Note that this model serves primarily as a language model and has not been validated nor finetuned as a general-purpose chat assistant. Thus further finetuning is required to properly work as an instruction-tuned model.
-## ● details
-> No longer promoting arithmark, after doing proper evals, falcon-instruct is even slighly better.
-Second spot on [BananaMindBench Leaderboard](https://huggingface.co/spaces/BananaMind/BananaMindBench-Leaderboard)!!🎉🎉
 <img
   class="benchmark-image"

 We celebrate three years of experiments with the release of `palmer-006`, our flagship and extremely small language model for local experimentation, edge hardware, small SBCs, and low-power devices. Note that this model serves primarily as a language model and has not been validated nor finetuned as a general-purpose chat assistant. Thus further finetuning is required to properly work as an instruction-tuned model.
+## updates
+- No longer promoting arithmark, after doing proper evals, falcon-instruct is even slighly better. Model is [#5 on open slm leaderboard](https://huggingface.co/spaces/AxiomicLabs/Open_SLM_Leaderboard)
+- Second spot on [BananaMindBench Leaderboard](https://huggingface.co/spaces/BananaMind/BananaMindBench-Leaderboard)!!🎉🎉
+## ● details
 <img
   class="benchmark-image"