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

Libraries

How to use FreelancerFel/TestLLAMA with Transformers:

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

pipe = pipeline("text-generation", model="FreelancerFel/TestLLAMA")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FreelancerFel/TestLLAMA")
model = AutoModelForCausalLM.from_pretrained("FreelancerFel/TestLLAMA", device_map="auto")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FreelancerFel/TestLLAMA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FreelancerFel/TestLLAMA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FreelancerFel/TestLLAMA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/FreelancerFel/TestLLAMA

SGLang

How to use FreelancerFel/TestLLAMA 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 "FreelancerFel/TestLLAMA" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FreelancerFel/TestLLAMA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "FreelancerFel/TestLLAMA" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FreelancerFel/TestLLAMA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

TestLLAMA

File size: 924 Bytes

5cfbc71

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  "best_metric": null,
  "best_model_checkpoint": null,
  "epoch": 1.0,
  "global_step": 16,
  "is_hyper_param_search": false,
  "is_local_process_zero": true,
  "is_world_process_zero": true,
  "log_history": [
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      "epoch": 0.19,
      "learning_rate": 0.00018571428571428572,
      "loss": 1.4494,
      "step": 3
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      "learning_rate": 0.00014285714285714287,
      "loss": 1.1933,
      "step": 6
    },
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      "learning_rate": 0.0001,
      "loss": 1.1962,
      "step": 9
    },
    {
      "epoch": 0.75,
      "learning_rate": 5.714285714285714e-05,
      "loss": 1.1429,
      "step": 12
    },
    {
      "epoch": 0.94,
      "learning_rate": 1.4285714285714285e-05,
      "loss": 1.3375,
      "step": 15
    }
  ],
  "max_steps": 16,
  "num_train_epochs": 1,
  "total_flos": 332044324306944.0,
  "trial_name": null,
  "trial_params": null
}