Instructions to use jprivera44/mo9c_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jprivera44/mo9c_lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/Llama-3.3-70B-Instruct")
model = PeftModel.from_pretrained(base_model, "jprivera44/mo9c_lora")

Transformers

How to use jprivera44/mo9c_lora with Transformers:

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

pipe = pipeline("text-generation", model="jprivera44/mo9c_lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("jprivera44/mo9c_lora", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use jprivera44/mo9c_lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jprivera44/mo9c_lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jprivera44/mo9c_lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jprivera44/mo9c_lora

SGLang

How to use jprivera44/mo9c_lora 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 "jprivera44/mo9c_lora" \
    --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": "jprivera44/mo9c_lora",
		"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 "jprivera44/mo9c_lora" \
        --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": "jprivera44/mo9c_lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use jprivera44/mo9c_lora with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for jprivera44/mo9c_lora to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for jprivera44/mo9c_lora to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for jprivera44/mo9c_lora to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="jprivera44/mo9c_lora",
    max_seq_length=2048,
)

Docker Model Runner
How to use jprivera44/mo9c_lora with Docker Model Runner:
```
docker model run hf.co/jprivera44/mo9c_lora
```

jprivera44 commited on 21 days ago

Commit

cd39c35

verified ·

1 Parent(s): f285baa

Upload training_config.yaml with huggingface_hub

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training_config.yaml +25 -0

training_config.yaml ADDED Viewed

	@@ -0,0 +1,25 @@

+run_id: mo9c
+data:
+  path: experiments/260409_b200_unsloth/data/mo9c/train_36k_combined.jsonl
+model:
+  name: unsloth/Llama-3.3-70B-Instruct
+training:
+  epochs: 1
+  batch_size: 8
+  gradient_accumulation_steps: 1
+  learning_rate: 2.0e-05
+  lora_seed: 42
+  shuffle_seed: 42
+  max_seq_length: 3072
+  save_total_limit: 1
+lora:
+  rank: 64
+  alpha: 64
+  dropout: 0.0
+  target_modules: all-linear
+logging:
+  wandb_project: collusion-mo-finetune
+  wandb_run_name: mo9c
+  require_wandb: true
+  log_every_n_steps: 1
+  save_every_n_steps: 500