Text Generation
Transformers
Safetensors
GGUF
English
falcon_h1
palmer
text-editing
rewriting
paraphrasing
grammar-correction
edge
small-language-model
Instructions to use appvoid/palmer-006 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
Upload README.md
Browse files
README.md
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+
transform: rotate(720deg) scale(1.08);
|
| 247 |
+
filter:
|
| 248 |
+
drop-shadow(0 0 10px rgba(255, 255, 255, 0.9))
|
| 249 |
+
drop-shadow(0 0 28px rgba(255, 255, 255, 0.48));
|
| 250 |
+
|
| 251 |
+
animation: singularity-ring 900ms linear 720ms infinite;
|
| 252 |
+
transition-delay: 0s;
|
| 253 |
}
|
| 254 |
|
| 255 |
.logo-stage:hover .logo-vortex::after {
|
| 256 |
opacity: 0.72;
|
| 257 |
+
transform: rotate(-540deg) scale(1.18);
|
|
|
|
|
|
|
| 258 |
|
| 259 |
+
animation: singularity-ring-reverse 1.35s linear 720ms infinite;
|
| 260 |
+
transition-delay: 0s;
|
| 261 |
+
}
|
|
|
|
| 262 |
|
| 263 |
+
@keyframes singularity-ring {
|
| 264 |
+
from {
|
| 265 |
+
transform: rotate(720deg) scale(1.08);
|
| 266 |
}
|
| 267 |
|
| 268 |
+
to {
|
| 269 |
+
transform: rotate(1080deg) scale(1.08);
|
| 270 |
}
|
| 271 |
}
|
| 272 |
|
| 273 |
+
@keyframes singularity-ring-reverse {
|
| 274 |
+
from {
|
| 275 |
+
transform: rotate(-540deg) scale(1.18);
|
|
|
|
|
|
|
|
|
|
|
|
|
| 276 |
}
|
| 277 |
|
| 278 |
+
to {
|
| 279 |
+
transform: rotate(-900deg) scale(1.18);
|
| 280 |
}
|
| 281 |
}
|
| 282 |
|
| 283 |
+
/* Benchmark remains centered and enlarges substantially on hover. */
|
| 284 |
.benchmark-image {
|
| 285 |
display: block;
|
| 286 |
position: static;
|
| 287 |
|
| 288 |
+
width: min(76%, 760px);
|
| 289 |
height: auto;
|
| 290 |
max-width: 100%;
|
| 291 |
margin: 2.5rem auto;
|
|
|
|
| 294 |
border-radius: 0.5rem;
|
| 295 |
outline: none;
|
| 296 |
|
| 297 |
+
user-select: none;
|
| 298 |
+
-webkit-user-select: none;
|
| 299 |
+
-moz-user-select: none;
|
| 300 |
+
-webkit-user-drag: none;
|
| 301 |
+
|
| 302 |
filter: none !important;
|
| 303 |
transform: scale(1) !important;
|
| 304 |
animation: none !important;
|
| 305 |
box-shadow: none !important;
|
| 306 |
|
| 307 |
+
position: relative;
|
| 308 |
+
z-index: 1;
|
| 309 |
+
transform-origin: center;
|
| 310 |
+
|
| 311 |
transition:
|
| 312 |
+
transform 420ms cubic-bezier(0.16, 1, 0.3, 1),
|
| 313 |
+
box-shadow 420ms ease,
|
| 314 |
+
filter 420ms ease !important;
|
| 315 |
}
|
| 316 |
|
| 317 |
.benchmark-image:hover {
|
|
|
|
| 320 |
outline-offset: 0;
|
| 321 |
|
| 322 |
filter: none !important;
|
| 323 |
+
z-index: 8;
|
| 324 |
+
transform: scale(1.28) !important;
|
| 325 |
animation: none !important;
|
| 326 |
+
filter: contrast(1.05) brightness(1.03) !important;
|
| 327 |
+
box-shadow:
|
| 328 |
+
0 24px 80px rgba(0, 0, 0, 0.55),
|
| 329 |
+
0 0 48px rgba(255, 255, 255, 0.08) !important;
|
| 330 |
}
|
| 331 |
|
| 332 |
p,
|
|
|
|
| 386 |
|
| 387 |
@media (prefers-reduced-motion: reduce) {
|
| 388 |
.logo-vortex,
|
|
|
|
| 389 |
.logo-vortex::before,
|
| 390 |
.logo-vortex::after,
|
| 391 |
+
.logo-image,
|
| 392 |
+
.benchmark-image {
|
| 393 |
+
animation: none !important;
|
| 394 |
+
transition: none !important;
|
|
|
|
| 395 |
}
|
| 396 |
}
|
| 397 |
</style>
|