Instructions to use panterarocks49/paul-graham-llama-3.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use panterarocks49/paul-graham-llama-3.1 with Transformers:

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

pipe = pipeline("text-generation", model="panterarocks49/paul-graham-llama-3.1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("panterarocks49/paul-graham-llama-3.1")
model = AutoModelForCausalLM.from_pretrained("panterarocks49/paul-graham-llama-3.1", device_map="auto")
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 panterarocks49/paul-graham-llama-3.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "panterarocks49/paul-graham-llama-3.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "panterarocks49/paul-graham-llama-3.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/panterarocks49/paul-graham-llama-3.1

SGLang

How to use panterarocks49/paul-graham-llama-3.1 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 "panterarocks49/paul-graham-llama-3.1" \
    --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": "panterarocks49/paul-graham-llama-3.1",
		"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 "panterarocks49/paul-graham-llama-3.1" \
        --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": "panterarocks49/paul-graham-llama-3.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use panterarocks49/paul-graham-llama-3.1 with Docker Model Runner:
```
docker model run hf.co/panterarocks49/paul-graham-llama-3.1
```

See axolotl config

axolotl version: 0.4.1

# This is an axolotl config that allowed creation of a model knowledgeable about 19th century warfare.

# Rent a GPU with a compute provider like Vast.ai or Runpod
# (Make sure it is using the axolotl docker image --- winglian/axolotl:main-latest)
# Copy this file over to the rented instance, in the /workspace/axolotl directory
# If running on a single-GPU setup, you must run:
# conda install -c conda-forge mpi4py mpich
# Then run this command from the /workspace/axolotl directory:
# accelerate launch --use_deepspeed -m axolotl.cli.train axolotl_config_19th_century_military_llama_3_jun_29.yaml

# If using GaLore, do not use deepspeed

# (to copy files over to a rented GPU instance, you'll have to use SSH to Secure CoPy files over from your machine to the rented one. This is what such a command might look like, adapt it to your needs)
# scp -P 40001 -r ./ root@173.231.62.170:/workspace/axolotl/

base_model: NousResearch/Meta-Llama-3.1-8B
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: json
    data_files: pretraining.jsonl
    ds_type: json
    type: completion
  - path: json
    data_files: simplified_data_rag.jsonl
    ds_type: json
    type: sharegpt
    conversation: chatml
  - path: json
    data_files: simplified_data_no_rag.jsonl
    ds_type: json
    type: sharegpt
    conversation: chatml

dataset_prepared_path: last_run_prepared
output_dir: ./pg_bot

sequence_len: 4500
sample_packing: true
pad_to_sequence_len: true

wandb_project: pg-bot-run2
wandb_entity:
wandb_watch:
wandb_run_id:
wandb_log_model:

gradient_accumulation_steps: 2
micro_batch_size: 1
num_epochs: 6
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5
noisy_embedding_alpha: 0 # no noisy embedding to ensure maximal memorization 

train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false

gradient_checkpointing: unsloth
early_stopping_patience:
resume_from_checkpoint: 
logging_steps: 1
xformers_attention:
flash_attention: true

chat_template: chatml

warmup_steps: 10
auto_resume_from_checkpoints: false
eval_steps: 10
saves_per_epoch: 1
eval_sample_packing: false
save_total_limit: 4
debug:
deepspeed: deepspeed_configs/zero2.json
special_tokens:
  pad_token: "<|end_of_text|>"

pg_bot

This model is a fine-tuned version of NousResearch/Meta-Llama-3.1-8B on the None dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 6
gradient_accumulation_steps: 2
total_train_batch_size: 12
total_eval_batch_size: 6
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 10
num_epochs: 6

Training results

Framework versions

Transformers 4.45.2
Pytorch 2.3.1+cu121
Datasets 3.0.1
Tokenizers 0.20.1

Downloads last month: 10

Model tree for panterarocks49/paul-graham-llama-3.1

Base model

NousResearch/Meta-Llama-3.1-8B

Finetuned

(6)

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