Instructions to use NousResearch/Yarn-Llama-2-70b-32k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NousResearch/Yarn-Llama-2-70b-32k with Transformers:

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

pipe = pipeline("text-generation", model="NousResearch/Yarn-Llama-2-70b-32k", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NousResearch/Yarn-Llama-2-70b-32k", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("NousResearch/Yarn-Llama-2-70b-32k", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use NousResearch/Yarn-Llama-2-70b-32k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NousResearch/Yarn-Llama-2-70b-32k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NousResearch/Yarn-Llama-2-70b-32k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/NousResearch/Yarn-Llama-2-70b-32k

SGLang

How to use NousResearch/Yarn-Llama-2-70b-32k 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 "NousResearch/Yarn-Llama-2-70b-32k" \
    --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": "NousResearch/Yarn-Llama-2-70b-32k",
		"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 "NousResearch/Yarn-Llama-2-70b-32k" \
        --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": "NousResearch/Yarn-Llama-2-70b-32k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use NousResearch/Yarn-Llama-2-70b-32k with Docker Model Runner:
```
docker model run hf.co/NousResearch/Yarn-Llama-2-70b-32k
```

emozilla commited on Nov 17, 2023

Commit

4699e73

1 Parent(s): e610773

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@@ -47,10 +47,10 @@ Long context benchmarks:
 | [Yarn-Llama-2-70b-32k](https://huggingface.co/NousResearch/Yarn-Llama-2-70b-32k) | 32k | 3.61 | 3.22 | 2.91 | 2.82 | 2.45 | 2.23 |
 Short context benchmarks showing that quality degradation is minimal:
-| Model | Context Window | ARC-c | Hellaswag | MMLU | Truthful QA |
-|-------|---------------:|------:|----------:|-----:|------------:|
-| [Llama-2-70b-hf](meta-llama/Llama-2-70b-hf) | 4k | 67.32 | 87.33 | 69.83 | 44.92 |
-| [Yarn-Llama-2-70b-32k](https://huggingface.co/NousResearch/Yarn-Llama-2-70b-32k) | 32k | 67.41 | - | 68.84 | - |
 ## Collaborators

 | [Yarn-Llama-2-70b-32k](https://huggingface.co/NousResearch/Yarn-Llama-2-70b-32k) | 32k | 3.61 | 3.22 | 2.91 | 2.82 | 2.45 | 2.23 |
 Short context benchmarks showing that quality degradation is minimal:
+| Model | Context Window | ARC-c | MMLU | Truthful QA |
+|-------|---------------:|------:|-----:|------------:|
+| [Llama-2-70b-hf](meta-llama/Llama-2-70b-hf) | 4k | 67.32 | 69.83 | 44.92 |
+| [Yarn-Llama-2-70b-32k](https://huggingface.co/NousResearch/Yarn-Llama-2-70b-32k) | 32k | 67.41 | 68.84 | 46.14 |
 ## Collaborators