Instructions to use mattshumer/ref_70_e3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mattshumer/ref_70_e3 with Transformers:

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

pipe = pipeline("text-generation", model="mattshumer/ref_70_e3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mattshumer/ref_70_e3")
model = AutoModelForCausalLM.from_pretrained("mattshumer/ref_70_e3")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mattshumer/ref_70_e3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mattshumer/ref_70_e3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mattshumer/ref_70_e3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mattshumer/ref_70_e3

SGLang

How to use mattshumer/ref_70_e3 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 "mattshumer/ref_70_e3" \
    --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": "mattshumer/ref_70_e3",
		"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 "mattshumer/ref_70_e3" \
        --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": "mattshumer/ref_70_e3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mattshumer/ref_70_e3 with Docker Model Runner:
```
docker model run hf.co/mattshumer/ref_70_e3
```

tokenizer and chat template fix for .gguf only

by nisten - opened Sep 9, 2024

base: refs/heads/main

←

from: refs/pr/9

Discussion Files changed

-2

nisten

Sep 9, 2024

there may be a better way to do this so it does multiple chat templates for both function calling and chat and benchmarking.

I suspect the older tool use template got replaced by the system prompt. But this one I tested and works fine with .gguf only

tokenizer and chat template fix for .gguf only, don't merge70942b67

nisten changed pull request title from tokenizer and chat template fix for .gguf only, don't merge to tokenizer and chat template fix for .gguf only Sep 9, 2024

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