Instructions to use mrm8488/tinyllama-bnb-4bit-ft-codeAlpaca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mrm8488/tinyllama-bnb-4bit-ft-codeAlpaca with Transformers:

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

pipe = pipeline("text-generation", model="mrm8488/tinyllama-bnb-4bit-ft-codeAlpaca")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mrm8488/tinyllama-bnb-4bit-ft-codeAlpaca")
model = AutoModelForCausalLM.from_pretrained("mrm8488/tinyllama-bnb-4bit-ft-codeAlpaca")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use mrm8488/tinyllama-bnb-4bit-ft-codeAlpaca with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mrm8488/tinyllama-bnb-4bit-ft-codeAlpaca"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mrm8488/tinyllama-bnb-4bit-ft-codeAlpaca",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mrm8488/tinyllama-bnb-4bit-ft-codeAlpaca

SGLang

How to use mrm8488/tinyllama-bnb-4bit-ft-codeAlpaca 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 "mrm8488/tinyllama-bnb-4bit-ft-codeAlpaca" \
    --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": "mrm8488/tinyllama-bnb-4bit-ft-codeAlpaca",
		"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 "mrm8488/tinyllama-bnb-4bit-ft-codeAlpaca" \
        --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": "mrm8488/tinyllama-bnb-4bit-ft-codeAlpaca",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mrm8488/tinyllama-bnb-4bit-ft-codeAlpaca with Docker Model Runner:
```
docker model run hf.co/mrm8488/tinyllama-bnb-4bit-ft-codeAlpaca
```

mrm8488 commited on Jan 9, 2024

Commit

b2709c1

1 Parent(s): 805f816

Update README.md

Browse files

Files changed (1) hide show

README.md +7 -16

README.md CHANGED Viewed

@@ -51,21 +51,12 @@ The following hyperparameters were used during training:
 | Training Loss | Epoch | Step | Validation Loss |
 |:-------------:|:-----:|:----:|:---------------:|
-| 1.7027        | 0.33  | 20   | 1.6844          |
-| 1.2736        | 0.66  | 40   | 1.2825          |
-| 1.091         | 0.99  | 60   | 1.0799          |
-| 0.9683        | 1.32  | 80   | 0.9900          |
-| 0.9431        | 1.65  | 100  | 0.9328          |
-| 0.9352        | 1.98  | 120  | 0.8907          |
-| 0.857         | 2.31  | 140  | 0.8580          |
-| 0.8316        | 2.64  | 160  | 0.8346          |
-| 0.8351        | 2.98  | 180  | 0.8203          |
-| 0.8204        | 3.31  | 200  | 0.8103          |
-| 0.7888        | 3.64  | 220  | 0.8035          |
-| 0.7677        | 3.97  | 240  | 0.7985          |
-| 0.7753        | 4.3   | 260  | 0.7952          |
-| 0.7912        | 4.63  | 280  | 0.7931          |
-| 0.7631        | 4.96  | 300  | 0.7926          |
 ### Framework versions
@@ -73,4 +64,4 @@ The following hyperparameters were used during training:
 - Transformers 4.37.0.dev0
 - Pytorch 2.1.0+cu121
 - Datasets 2.16.1
-- Tokenizers 0.15.0

 | Training Loss | Epoch | Step | Validation Loss |
 |:-------------:|:-----:|:----:|:---------------:|
+| 1.1517        | 0.83  | 50   | 1.1598          |
+| 0.9428        | 1.65  | 100  | 0.9327          |
+| 0.8319        | 2.48  | 150  | 0.8448          |
+| 0.8205        | 3.31  | 200  | 0.8102          |
+| 0.7977        | 4.13  | 250  | 0.7966          |
+| 0.763         | 4.96  | 300  | 0.7926          |
 ### Framework versions
 - Transformers 4.37.0.dev0
 - Pytorch 2.1.0+cu121
 - Datasets 2.16.1
+- Tokenizers 0.15.0