Instructions to use diabolic6045/open-llama-3.2-1B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use diabolic6045/open-llama-3.2-1B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="diabolic6045/open-llama-3.2-1B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("diabolic6045/open-llama-3.2-1B-Instruct")
model = AutoModelForMultimodalLM.from_pretrained("diabolic6045/open-llama-3.2-1B-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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use diabolic6045/open-llama-3.2-1B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "diabolic6045/open-llama-3.2-1B-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": "diabolic6045/open-llama-3.2-1B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/diabolic6045/open-llama-3.2-1B-Instruct

SGLang

How to use diabolic6045/open-llama-3.2-1B-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 "diabolic6045/open-llama-3.2-1B-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": "diabolic6045/open-llama-3.2-1B-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 "diabolic6045/open-llama-3.2-1B-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": "diabolic6045/open-llama-3.2-1B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use diabolic6045/open-llama-3.2-1B-Instruct with Docker Model Runner:
```
docker model run hf.co/diabolic6045/open-llama-3.2-1B-Instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

open-llama-Instruct

This model is a fine-tuned version of meta-llama/Llama-3.2-1B-Instruct on the diabolic6045/OpenHermes-2.5_alpaca_10 dataset. which is 10% of OpenHermes 2.5 Dataset

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
distributed_type: multi-GPU
num_devices: 2
total_train_batch_size: 4
total_eval_batch_size: 4
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 10
num_epochs: 1
mixed_precision_training: Native AMP

Training results

Groups	Version	Filter	Metric		Value		Stderr
mmlu	2	none	acc	↑	0.3632	±	0.0040
- humanities	2	none	acc	↑	0.3411	±	0.0068
- other	2	none	acc	↑	0.4078	±	0.0087
- social sciences	2	none	acc	↑	0.3997	±	0.0087
- stem	2	none	acc	↑	0.3165	±	0.0082

Framework versions

Transformers 4.45.2
Pytorch 2.1.2
Datasets 3.0.1
Tokenizers 0.20.1

See axolotl config

axolotl version: 0.4.1


base_model: meta-llama/Llama-3.2-1B-Instruct

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: diabolic6045/OpenHermes-2.5_alpaca_10
    type: alpaca
dataset_prepared_path: last_run_prepared
val_set_size: 0
output_dir: ./outputs/out
hub_model_id: diabolic6045/open-llama-Instruct
hf_use_auth_token: true

sequence_len: 1024
sample_packing: true
pad_to_sequence_len: true

wandb_project: open-llama
wandb_entity: 
wandb_watch: all
wandb_name: open-llama
wandb_log_model: 

gradient_accumulation_steps: 1
micro_batch_size: 2
num_epochs: 1

optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: false

warmup_steps: 10
evals_per_epoch: 2
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  pad_token: <|end_of_text|>

Downloads last month: 7

Safetensors

Model size

1B params

Tensor type

F16

Model tree for diabolic6045/open-llama-3.2-1B-Instruct

Base model

meta-llama/Llama-3.2-1B-Instruct

Finetuned

(1757)

this model

Merges

34 models

Quantizations

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open-llama-3.2-1B-Instruct