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

Libraries

How to use ambrosfitz/phi-history with Transformers:

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

pipe = pipeline("text-generation", model="ambrosfitz/phi-history", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ambrosfitz/phi-history", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("ambrosfitz/phi-history", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

PEFT
How to use ambrosfitz/phi-history with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ambrosfitz/phi-history with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ambrosfitz/phi-history"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ambrosfitz/phi-history",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ambrosfitz/phi-history

SGLang

How to use ambrosfitz/phi-history 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 "ambrosfitz/phi-history" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ambrosfitz/phi-history",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "ambrosfitz/phi-history" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ambrosfitz/phi-history",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ambrosfitz/phi-history with Docker Model Runner:
```
docker model run hf.co/ambrosfitz/phi-history
```

ambrosfitz commited on Apr 29, 2024

Commit

8c97b18

verified ·

1 Parent(s): 3a5565e

Update README.md

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README.md +0 -37

README.md CHANGED Viewed

@@ -5,41 +5,4 @@ tags:
 - text-generation
 - peft
 library_name: transformers
-widget:
-  - messages:
-      - role: user
-        content: What is your favorite condiment?
 license: other
----
-# Model Trained Using AutoTrain
-This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
-# Usage
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-model_path = "PATH_TO_THIS_REPO"
-tokenizer = AutoTokenizer.from_pretrained(model_path)
-model = AutoModelForCausalLM.from_pretrained(
-    model_path,
-    device_map="auto",
-    torch_dtype='auto'
-).eval()
-# Prompt content: "hi"
-messages = [
-    {"role": "user", "content": "hi"}
-]
-input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
-output_ids = model.generate(input_ids.to('cuda'))
-response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
-# Model response: "Hello! How can I assist you today?"
-print(response)
-```

 - text-generation
 - peft
 library_name: transformers
 license: other