Instructions to use jschwab21/Reflection-Llama-3.1-70B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jschwab21/Reflection-Llama-3.1-70B with Transformers:

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

pipe = pipeline("text-generation", model="jschwab21/Reflection-Llama-3.1-70B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jschwab21/Reflection-Llama-3.1-70B")
model = AutoModelForCausalLM.from_pretrained("jschwab21/Reflection-Llama-3.1-70B", device_map="auto")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jschwab21/Reflection-Llama-3.1-70B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jschwab21/Reflection-Llama-3.1-70B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jschwab21/Reflection-Llama-3.1-70B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jschwab21/Reflection-Llama-3.1-70B

SGLang

How to use jschwab21/Reflection-Llama-3.1-70B 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 "jschwab21/Reflection-Llama-3.1-70B" \
    --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": "jschwab21/Reflection-Llama-3.1-70B",
		"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 "jschwab21/Reflection-Llama-3.1-70B" \
        --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": "jschwab21/Reflection-Llama-3.1-70B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jschwab21/Reflection-Llama-3.1-70B with Docker Model Runner:
```
docker model run hf.co/jschwab21/Reflection-Llama-3.1-70B
```

Reflection-Llama-3.1-70B / README.md

jschwab21

Duplicate from mattshumer/Reflection-Llama-3.1-70B

4d90ad4 verified almost 2 years ago

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	---
	license: llama3.1
	base_model: meta-llama/Meta-Llama-3.1-70B-Instruct
	pipeline_tag: text-generation
	library_name: transformers
	---
	# Reflection Llama-3.1 70B

	\| IMPORTANT UPDATE – There was an issue with the model when we first uploaded it. If you tried it and didn't have good results, please, try again, we think we've fixed the issue.

	Reflection Llama-3.1 70B is (currently) the world's top open-source LLM, trained with a new technique called Reflection-Tuning that teaches a LLM to detect mistakes in its reasoning and correct course.

	The model was trained on synthetic data generated by [Glaive](https://glaive.ai). If you're training a model, Glaive is incredible — use them.

	You can [try the model here](https://reflection-playground-production.up.railway.app/).

	## Benchmarks
	![image/png](https://cdn-uploads.huggingface.co/production/uploads/60518f3731c5be7f3dd5ebc3/zNs-ZFs0SbnomH7mikiOU.png)

	All benchmarks tested have been checked for contamination by running [LMSys's LLM Decontaminator](https://github.com/lm-sys/llm-decontaminator). When benchmarking, we isolate the `<output>` and benchmark on solely that section.

	Trained from Llama 3.1 70B Instruct, you can sample from Reflection Llama-3.1 70B using the same code, pipelines, etc. as any other Llama model. It even uses the stock Llama 3.1 chat template format (though, we've trained in a few new special tokens to aid in reasoning and reflection).

	During sampling, the model will start by outputting reasoning inside `<thinking>` and `</thinking>` tags, and then once it is satisfied with its reasoning, it will output the final answer inside `<output>` and `</output>` tags. Each of these tags are special tokens, trained into the model.

	This enables the model to separate its internal thoughts and reasoning from its final answer, improving the experience for the user.

	Inside the `<thinking>` section, the model may output one or more `<reflection>` tags, which signals the model has caught an error in its reasoning and will attempt to correct it before providing a final answer.

	## System Prompt

	The system prompt used for training this model is:

	```
	You are a world-class AI system, capable of complex reasoning and reflection. Reason through the query inside <thinking> tags, and then provide your final response inside <output> tags. If you detect that you made a mistake in your reasoning at any point, correct yourself inside <reflection> tags.
	```

	We recommend using this exact system prompt to get the best results from Reflection Llama-3.1 70B. You may also want to experiment combining this system prompt with your own custom instructions to customize the behavior of the model.

	## Chat Format

	As mentioned above, the model uses the standard Llama 3.1 chat format. Here’s an example:

	```
	<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|>

	You are a world-class AI system, capable of complex reasoning and reflection. Reason through the query inside <thinking> tags, and then provide your final response inside <output> tags. If you detect that you made a mistake in your reasoning at any point, correct yourself inside <reflection> tags.<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>

	what is 2+2?<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>
	```

	## Tips for Performance

	- We are initially recommending a `temperature` of `.7` and a `top_p` of `.95`.
	- For increased accuracy, append `Think carefully.` at the end of your messages.

	## Dataset / Report

	Both the dataset and a brief report detailing how we trained this model will be released next week, alongside our Reflection 405B model that we expect will be the top-performing LLM in the world, including closed-source models.

	---

	Thanks to Jason Kuperberg and Josh Bickett from the [HyperWrite](https://hyperwriteai.com) team for reviewing drafts of the report we'll be releasing next week.

	Also, we know right now the model is split into a ton of files. We'll condense this soon to make the model easier to download and work with!