Instructions to use mrm8488/phi-2-coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mrm8488/phi-2-coder with Transformers:

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

pipe = pipeline("text-generation", model="mrm8488/phi-2-coder", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("mrm8488/phi-2-coder", trust_remote_code=True, dtype="auto")

MLX

How to use mrm8488/phi-2-coder with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm
# if on a CUDA device, also pip install mlx[cuda]

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("mrm8488/phi-2-coder")

prompt = "Once upon a time in"
text = generate(model, tokenizer, prompt=prompt, verbose=True)

Inference
Notebooks
Google Colab
Kaggle
Local Apps
LM Studio

vLLM

How to use mrm8488/phi-2-coder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mrm8488/phi-2-coder"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mrm8488/phi-2-coder",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mrm8488/phi-2-coder

SGLang

How to use mrm8488/phi-2-coder 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/phi-2-coder" \
    --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/phi-2-coder",
		"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/phi-2-coder" \
        --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/phi-2-coder",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

MLX LM

How to use mrm8488/phi-2-coder with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Generate some text
mlx_lm.generate --model "mrm8488/phi-2-coder" --prompt "Once upon a time"

Docker Model Runner
How to use mrm8488/phi-2-coder with Docker Model Runner:
```
docker model run hf.co/mrm8488/phi-2-coder
```

mrm8488 commited on Dec 24, 2023

Commit

4ae69ae

1 Parent(s): 1bfb00d

Update README.md

Browse files

Files changed (1) hide show

README.md +8 -6

README.md CHANGED Viewed

@@ -76,12 +76,13 @@ eval_steps=50,
 | Step | Training Loss | Validation Loss |
-|------|----------|----------|
-| 50   | 0.624400 | 0.600070 |
-| 100  | 0.634100 | 0.592757 |
-| 150  | 0.545800 | 0.586652 |
-| 200  | 0.572500 | 0.577525 |
-| 250  | 0.528000 | 0.590118 |
 ### HumanEval results 📊
@@ -90,6 +91,7 @@ WIP
 ### Example of usage 👩‍💻
 ```py
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer

 | Step | Training Loss | Validation Loss |
+|------|---------------|-----------------|
+| 50   | 0.763100      | 0.717398        |
+| 100  | 0.673500      | 0.694871        |
+| 150  | 0.696000      | 0.689336        |
+| 200  | 0.786100      | 0.687515        |
+| 250  | 0.734600      | 0.686658        |
 ### HumanEval results 📊
 ### Example of usage 👩‍💻
 ```py
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer