Instructions to use karwalski/toke with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use karwalski/toke with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("karwalski/toke")
model = AutoModelForCausalLM.from_pretrained("karwalski/toke")
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 karwalski/toke with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/karwalski/toke

SGLang

How to use karwalski/toke 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 "karwalski/toke" \
    --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": "karwalski/toke",
		"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 "karwalski/toke" \
        --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": "karwalski/toke",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use karwalski/toke with Docker Model Runner:
```
docker model run hf.co/karwalski/toke
```

toke-7b-gate2

A 7B parameter language model fine-tuned to generate code in toke, a programming language designed to reduce token cost of AI-generated code. This model writes syntactically valid toke 100% of the time.

Model Details

Property	Value
Base model	Qwen 2.5 Coder 7B-Instruct
Method	QLoRA (rank 64, alpha 128, 3 epochs)
Training data	25,953 records — 18,890 synthetic + 6,069 from loke production (87K lines)
Training time	37 hours on NVIDIA A10G (24 GB)
Weights	AWQ 4-bit quantized (this repo)
Context length	32,768 tokens
License	Apache 2.0

What is toke?

toke is a statically typed, compiled language with a 55-character alphabet (lowercase a-z, digits 0-9, and 19 symbols). It compiles to native binaries via LLVM. A purpose-built BPE tokenizer achieves 52% fewer tokens on average vs cl100k_base.

13 keywords: m f t i if el lp br let mut as rt mt
No comments in source — documentation lives in companion files (.tkc.md)
Errors as values — no exceptions, result types with mt (match)
Website: tokelang.dev | Console: console.tokelang.dev

Gate 2 Results (May 2026)

Metric	Gate 1	Gate 2
Compilation Pass@1	63.7%	100%
Tasks evaluated	1,000	700
Functional Pass@1	—	~8%
Training records	73,000	25,953

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("karwalski/toke", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("karwalski/toke")

prompt = """<|im_start|>system
Write toke programs. m=mod; f=name(p:type):ret{body}; let x=42; <expr return. Semicolons everywhere. Start with m=.
<|im_end|>
<|im_start|>user
Write a hello world program
<|im_end|>
<|im_start|>assistant
"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.2, do_sample=True)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
# m=hello;i=io:std.io;f=main():i64{io.println("hello world");<0};

API Access

Free API access via console.tokelang.dev — no credit card required.

curl -X POST https://api.tokelang.dev/v1/generate \
  -H "X-Api-Key: YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{"description": "Return the absolute value of an integer"}'

Model tree for karwalski/toke

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-Coder-7B

Finetuned

Qwen/Qwen2.5-Coder-7B-Instruct

Quantized

(191)

this model

Evaluation results

Compilation Pass@1
self-reported

100.000
Functional Pass@1
self-reported

8.000

karwalski
/

toke