Instructions to use Ramikan-BR/tinyllama_PY-CODER-bnb-4bit-lora_model-4k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ramikan-BR/tinyllama_PY-CODER-bnb-4bit-lora_model-4k with Transformers:

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

pipe = pipeline("text-generation", model="Ramikan-BR/tinyllama_PY-CODER-bnb-4bit-lora_model-4k")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Ramikan-BR/tinyllama_PY-CODER-bnb-4bit-lora_model-4k")
model = AutoModelForCausalLM.from_pretrained("Ramikan-BR/tinyllama_PY-CODER-bnb-4bit-lora_model-4k", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Ramikan-BR/tinyllama_PY-CODER-bnb-4bit-lora_model-4k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ramikan-BR/tinyllama_PY-CODER-bnb-4bit-lora_model-4k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ramikan-BR/tinyllama_PY-CODER-bnb-4bit-lora_model-4k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Ramikan-BR/tinyllama_PY-CODER-bnb-4bit-lora_model-4k

SGLang

How to use Ramikan-BR/tinyllama_PY-CODER-bnb-4bit-lora_model-4k 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 "Ramikan-BR/tinyllama_PY-CODER-bnb-4bit-lora_model-4k" \
    --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": "Ramikan-BR/tinyllama_PY-CODER-bnb-4bit-lora_model-4k",
		"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 "Ramikan-BR/tinyllama_PY-CODER-bnb-4bit-lora_model-4k" \
        --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": "Ramikan-BR/tinyllama_PY-CODER-bnb-4bit-lora_model-4k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Desktop
Docker Model Runner
How to use Ramikan-BR/tinyllama_PY-CODER-bnb-4bit-lora_model-4k with Docker Model Runner:
```
docker model run hf.co/Ramikan-BR/tinyllama_PY-CODER-bnb-4bit-lora_model-4k
```

Ramikan-BR commited on Apr 25, 2024

Commit

ce792c2

verified ·

1 Parent(s): 96986db

Update README.md

Browse files

Files changed (1) hide show

README.md +30 -5

README.md CHANGED Viewed

@@ -21,11 +21,36 @@ Accumulation steps = 16 \ / Total batch size = 32 | Total steps = 30 "-____-"
 Number of trainable parameters = 100,925,440 [30/30 26:26, Epoch 0/1] Step
 Training Loss
-1 1.737000 2 1.738000 3 1.384700 4 1.086400 5 1.009600 6 0.921000 7 0.830400 8
-0.808900 9 0.774500 10 0.759900 11 0.736100 12 0.721200 13 0.733200 14 0.701000
-15 0.711700 16 0.701400 17 0.689500 18 0.678800 19 0.675200 20 0.680500 21
-0.685800 22 0.681200 23 0.672000 24 0.679900 25 0.675500 26 0.666600 27 0.687900
-28 0.653600 29 0.672500 30 0.660900
 ---
 null

 Number of trainable parameters = 100,925,440 [30/30 26:26, Epoch 0/1] Step
 Training Loss
+1 1.737000
+2 1.738000
+3 1.384700
+4 1.086400
+5 1.009600
+6 0.921000
+7 0.830400
+8 0.808900
+9 0.774500
+10 0.759900
+11 0.736100
+12 0.721200
+13 0.733200
+14 0.701000
+15 0.711700
+16 0.701400
+17 0.689500
+18 0.678800
+19 0.675200
+20 0.680500
+21 0.685800
+22 0.681200
+23 0.672000
+24 0.679900
+25 0.675500
+26 0.666600
+27 0.687900
+28 0.653600
+29 0.672500
+30 0.660900
 ---
 null