Instructions to use kehanlu/llama-3.2-8B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kehanlu/llama-3.2-8B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="kehanlu/llama-3.2-8B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kehanlu/llama-3.2-8B-Instruct")
model = AutoModelForCausalLM.from_pretrained("kehanlu/llama-3.2-8B-Instruct")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use kehanlu/llama-3.2-8B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kehanlu/llama-3.2-8B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kehanlu/llama-3.2-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kehanlu/llama-3.2-8B-Instruct

SGLang

How to use kehanlu/llama-3.2-8B-Instruct 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 "kehanlu/llama-3.2-8B-Instruct" \
    --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": "kehanlu/llama-3.2-8B-Instruct",
		"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 "kehanlu/llama-3.2-8B-Instruct" \
        --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": "kehanlu/llama-3.2-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use kehanlu/llama-3.2-8B-Instruct with Docker Model Runner:
```
docker model run hf.co/kehanlu/llama-3.2-8B-Instruct
```

This repository contains the text-only LLM portion of meta-llama/Llama-3.2-11B-Vision-Instruct

How it was done

from collections import OrderedDict
from transformers import MllamaForConditionalGeneration, AutoModelForCausalLM
from transformers.models.mllama.modeling_mllama import MllamaCrossAttentionDecoderLayer
llama32_id = "meta-llama/Llama-3.2-11B-Vision-Instruct"
llama32 = MllamaForConditionalGeneration.from_pretrained(
    llama32_id,
    torch_dtype=torch.bfloat16,
    device_map="cuda:0",
)


new_layers = []
for idx, layer in enumerate(llama32.language_model.model.layers):
    if isinstance(layer, MllamaCrossAttentionDecoderLayer):
        # CrossAttention layers are only take effect when image is provided.
        # Ignore here since we want text-only model
        pass
    else:
        new_layers.append(layer)
llama32.language_model.model.cross_attention_layers = []
llama32.language_model.model.layers = torch.nn.ModuleList(new_layers)


# Now llama32.language_model is identical to Llama3.1-8B-Instruct, except the embedding size(+8)
# see: https://github.com/huggingface/transformers/blob/a22a4378d97d06b7a1d9abad6e0086d30fdea199/src/transformers/models/mllama/modeling_mllama.py#L1667C9-L1667C26
new_llama32_state_dict = OrderedDict()
for k, v in llama32.language_model.state_dict().items():
    if k == "model.embed_tokens.weight":
        v = v[:128256, :]
    new_llama32_state_dict[k] = v


# Load a llama31 for the architecture
llama31_id = "meta-llama/Llama-3.1-8B-Instruct"
llama31 = AutoModelForCausalLM.from_pretrained(
    llama31_id,
    torch_dtype=torch.bfloat16,
    device_map="cuda:1",
)

llama31.load_state_dict(new_llama32_state_dict)
# <All keys matched successfully>

llama31.save_pretrained("./my-cool-llama3.2")

Note:

In the original tokenizer, there are date_string in tokenizer.chat_template (which append the current date when calling tokenizer.apply_chat_template(messages)).

I removed this behavior in this repo. Please be aware when you use AutoTokenizer.from_pretrained(this_repo).

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Safetensors

Model size

8B params

Tensor type

BF16