Instructions to use JairoDanielMT/Llama2-Fine-Tuning-python-codes-25k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JairoDanielMT/Llama2-Fine-Tuning-python-codes-25k with Transformers:

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

pipe = pipeline("text-generation", model="JairoDanielMT/Llama2-Fine-Tuning-python-codes-25k")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("JairoDanielMT/Llama2-Fine-Tuning-python-codes-25k")
model = AutoModelForCausalLM.from_pretrained("JairoDanielMT/Llama2-Fine-Tuning-python-codes-25k")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use JairoDanielMT/Llama2-Fine-Tuning-python-codes-25k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JairoDanielMT/Llama2-Fine-Tuning-python-codes-25k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JairoDanielMT/Llama2-Fine-Tuning-python-codes-25k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/JairoDanielMT/Llama2-Fine-Tuning-python-codes-25k

SGLang

How to use JairoDanielMT/Llama2-Fine-Tuning-python-codes-25k 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 "JairoDanielMT/Llama2-Fine-Tuning-python-codes-25k" \
    --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": "JairoDanielMT/Llama2-Fine-Tuning-python-codes-25k",
		"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 "JairoDanielMT/Llama2-Fine-Tuning-python-codes-25k" \
        --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": "JairoDanielMT/Llama2-Fine-Tuning-python-codes-25k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use JairoDanielMT/Llama2-Fine-Tuning-python-codes-25k with Docker Model Runner:
```
docker model run hf.co/JairoDanielMT/Llama2-Fine-Tuning-python-codes-25k
```

Llama2-Fine-Tuning-python-codes-25k / preentrenado_python_code /README.md

JairoDanielMT

Subir modelo y lora del modelo llama 2 con el dataset de flytech/python-codes-25k

2091d19 verified about 2 years ago

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	---
	library_name: peft
	---
	## Training procedure


	The following `bitsandbytes` quantization config was used during training:
	- load_in_8bit: False
	- load_in_4bit: True
	- llm_int8_threshold: 6.0
	- llm_int8_skip_modules: None
	- llm_int8_enable_fp32_cpu_offload: False
	- llm_int8_has_fp16_weight: False
	- bnb_4bit_quant_type: nf4
	- bnb_4bit_use_double_quant: False
	- bnb_4bit_compute_dtype: float16
	### Framework versions


	- PEFT 0.4.0