Instructions to use appvoid/palmer-005-nano with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use appvoid/palmer-005-nano with Transformers:

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

pipe = pipeline("text-generation", model="appvoid/palmer-005-nano")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("appvoid/palmer-005-nano")
model = AutoModelForCausalLM.from_pretrained("appvoid/palmer-005-nano")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use appvoid/palmer-005-nano with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "appvoid/palmer-005-nano"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "appvoid/palmer-005-nano",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/appvoid/palmer-005-nano

SGLang

How to use appvoid/palmer-005-nano 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-005-nano" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "appvoid/palmer-005-nano",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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-005-nano" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "appvoid/palmer-005-nano",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use appvoid/palmer-005-nano with Docker Model Runner:
```
docker model run hf.co/appvoid/palmer-005-nano
```

palmer-005-nano / README.md

appvoid

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metadata

base_model: appvoid/graphite-004
library_name: transformers
tags:
  - text-generation
  - text-editing
  - rewriting
  - paraphrasing
  - instruct
private: true
license: other
datasets:
  - appvoid/rewrite
pipeline_tag: text-generation
extra_gated_prompt: >-
  You agree to not use this model (or future versions) to conduct experiments
  that cause harm to any person or group and to respect the LICENSE that comes
  with this repository.
extra_gated_fields:
  Company: text
  Country: country
  Specific date: date_picker
  I want to use this model for:
    type: select
    options:
      - Research
      - Education
      - Hobby
      - Commercial
      - label: Other
        value: other
  I agree to use this model in good faith ONLY: checkbox

In this repository, we propose the next iteration of palmer, a new family of small language models trained with better foundational models, better data but same tasks: Text rewriting, paraphrasing, tone transfer, grammar-style editing, and instruction-following text transformations. With only 90m parameters, this model is perfect for experiments on small SBCs and low-power devices.
Our custom evaluation consists of 100 text-editing tasks where the model has to modify a diversity of texts in different ways. All models were evaluated using q8_0 gguf quantization.

	
		
Model
Avg
Emb
String
Token
Rule
Exact
Strict
Flagged
Params

🥇 nano
84.70
95.81
81.12
78.22
87.57
39/100
68/100
19/100
90m

lfm2
72.04
93.19
62.00
56.22
75.35
21/100
40/100
35/100
700m

granite4
71.96
92.54
65.01
54.98
76.41
17/100
34/100
33/100
350m

qwen3
51.81
87.76
45.26
33.65
50.80
6/100
22/100
60/100
600m

	
The model has learned from a dataset with about 1 million examples on how to edit text in many different ways. The dataset contains high-quality data that was further expanded using common well-known heuristics.

	
		
	
	
		supporters
	

	
		
	
	
		legal
	
If you are an individual, you're totally free to make money with the model as long as you properly credit the model being used in your products. If you are a company, you need to get a license at this email for commercial purposes.
Note: the model has not been tested as a chat assistant and it might not work as intended, use with caution.

Model	Avg	Emb	String	Token	Rule	Exact	Strict	Flagged	Params
`🥇 nano`	84.70	95.81	81.12	78.22	87.57	39/100	68/100	19/100	90m
`lfm2`	72.04	93.19	62.00	56.22	75.35	21/100	40/100	35/100	700m
`granite4`	71.96	92.54	65.01	54.98	76.41	17/100	34/100	33/100	350m
`qwen3`	51.81	87.76	45.26	33.65	50.80	6/100	22/100	60/100	600m