Instructions to use k-l-lambda/Llama-3.2-1B-vocab32k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use k-l-lambda/Llama-3.2-1B-vocab32k with Transformers:

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

pipe = pipeline("text-generation", model="k-l-lambda/Llama-3.2-1B-vocab32k")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("k-l-lambda/Llama-3.2-1B-vocab32k")
model = AutoModelForCausalLM.from_pretrained("k-l-lambda/Llama-3.2-1B-vocab32k")
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 Settings

vLLM

How to use k-l-lambda/Llama-3.2-1B-vocab32k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "k-l-lambda/Llama-3.2-1B-vocab32k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "k-l-lambda/Llama-3.2-1B-vocab32k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/k-l-lambda/Llama-3.2-1B-vocab32k

SGLang

How to use k-l-lambda/Llama-3.2-1B-vocab32k 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 "k-l-lambda/Llama-3.2-1B-vocab32k" \
    --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": "k-l-lambda/Llama-3.2-1B-vocab32k",
		"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 "k-l-lambda/Llama-3.2-1B-vocab32k" \
        --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": "k-l-lambda/Llama-3.2-1B-vocab32k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use k-l-lambda/Llama-3.2-1B-vocab32k with Docker Model Runner:
```
docker model run hf.co/k-l-lambda/Llama-3.2-1B-vocab32k
```

Model Information

This is a vocab pruned variant of Llama-3.2-1B-Instruct. The vocabulary size is pruned from 128256 to 32256. The total parameter size is: 1,039,214,756, ~200M parameters pruned from origin.

How to use

This is a code example:

import torch
from transformers import pipeline


pipe = pipeline(
    "text-generation",
    model='k-l-lambda/Llama-3.2-1B-vocab32k',
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
messages = [
    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
    {"role": "user", "content": "Who are you?"},
]
outputs = pipe(
    messages,
    max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])

Another:

from transformers import AutoModelForCausalLM, AutoTokenizer


tokenizer = AutoTokenizer.from_pretrained("k-l-lambda/Llama-3.2-1B-vocab32k")
model = AutoModelForCausalLM.from_pretrained("k-l-lambda/Llama-3.2-1B-vocab32k")

input_ids = tokenizer.encode("Hello, ", return_tensors="pt")
output = model.generate(input_ids)
print(tokenizer.decode(output[0]))

Tokens conversion

You can map an ID value in 32k vocab to the ID value in original 128k vocab, by the tensor in token_indices.pt and inv_token_indices.pt.

import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer


tokenizer128k = AutoTokenizer.from_pretrained('meta-llama/Llama-3.2-1B-Instruct')
tokenizer32k = AutoTokenizer.from_pretrained('k-l-lambda/Llama-3.2-1B-vocab32k')

indices_path = hf_hub_download(repo_id='k-l-lambda/Llama-3.2-1B-vocab32k', filename='token_indices.pt')
inv_indices_path = hf_hub_download(repo_id='k-l-lambda/Llama-3.2-1B-vocab32k', filename='inv_token_indices.pt')
token_indices = torch.load(indices_path)
inv_token_indices = torch.load(inv_indices_path)

ids_32k = tokenizer32k.encode('This is an example sentence.')
ids_128k = [token_indices[id].item() for id in ids_32k]
print(f'{ids_32k=}')
print(f'{ids_128k=}')

print(tokenizer128k.decode(ids_128k))


ids_128k = tokenizer128k.encode('This is another example sentence.')
ids_32k = [inv_token_indices[id].item() for id in ids_128k]
print(f'{ids_128k=}')
print(f'{ids_32k=}')	# non-exist tokens in 32k vocab will map to -1

print(tokenizer32k.decode(ids_32k))

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Safetensors

Model size

1B params

Tensor type

BF16

k-l-lambda
/

Llama-3.2-1B-vocab32k

Model Information

How to use

Tokens conversion

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