Instructions to use bhenrym14/airoboros-l2-13b-2.1-YaRN-64k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bhenrym14/airoboros-l2-13b-2.1-YaRN-64k with Transformers:

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

pipe = pipeline("text-generation", model="bhenrym14/airoboros-l2-13b-2.1-YaRN-64k", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bhenrym14/airoboros-l2-13b-2.1-YaRN-64k", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("bhenrym14/airoboros-l2-13b-2.1-YaRN-64k", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use bhenrym14/airoboros-l2-13b-2.1-YaRN-64k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bhenrym14/airoboros-l2-13b-2.1-YaRN-64k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bhenrym14/airoboros-l2-13b-2.1-YaRN-64k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/bhenrym14/airoboros-l2-13b-2.1-YaRN-64k

SGLang

How to use bhenrym14/airoboros-l2-13b-2.1-YaRN-64k 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 "bhenrym14/airoboros-l2-13b-2.1-YaRN-64k" \
    --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": "bhenrym14/airoboros-l2-13b-2.1-YaRN-64k",
		"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 "bhenrym14/airoboros-l2-13b-2.1-YaRN-64k" \
        --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": "bhenrym14/airoboros-l2-13b-2.1-YaRN-64k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use bhenrym14/airoboros-l2-13b-2.1-YaRN-64k with Docker Model Runner:
```
docker model run hf.co/bhenrym14/airoboros-l2-13b-2.1-YaRN-64k
```

bhenrym14 commited on Sep 2, 2023

Commit

41e0890

1 Parent(s): 2631d5b

Update README.md

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@@ -27,18 +27,18 @@ Ooba use: Be sure to increase the `Truncate the prompt up to this length` parame
 ## Motivation
-[Yet another RoPE extensioN method (YARN)](https://github.com/jquesnelle/yarn/blob/master/paper/yarn.pdf) is a novel method of extending the useful context of pretrained LLMs, with architectures employing RoPE, with minimal additonal training requirements. This method is the consequence of efforts to mitigate the shortcomings of other methods such as Position Interpolation (PI) and NTK-Aware scaling. Since I am unaware of any existing instruction-tuned models which employ YaRN, I finetuned using Jon Durbin's latest airoboros dataset.
 ## Relative Performance (wikitext perplexity)
-| Context (tokens)  | **bhenrym14/airoboros-l2-13b-PI-16k-fp16** | bhenrym14/airophin-v2-13b-PI-8k-fp16 | bhenrym14/airophin-13b-pntk-16k-fp16| bhenrym14/airoboros-13b-gpt4-1.4.1-PI-8192-fp16 |bhenrym14/airoboros-33b-gpt4-1.4.1-lxctx-PI-16384-fp16  | jondurbin/airoboros-l2-13b-gpt4-1.4.1 |
-| --- | --- | ---| ----- | -----| ------| --- |
-| 512 | 7.67 | 7.38 | 7.62 | 8.24 | 7.90 | **7.23** |
-| 1024 | 6.15 | 5.99 | 6.20 | 6.71 | 6.17 | **5.85**  |
-| 2048 | 5.29 | 5.22 | 5.38 | 5.87 | 5.23 | **5.07** |
-| 4096 | 4.94 | 4.90 | 5.08 | 5.50 | 4.91 | **4.77** |
-| 8192 | **4.71** | **4.71** | 4.90 | 5.32 | Not Tested | 57.1 |
-| 12000 | **4.54** | 55 | 4.82 | 56.1 | Not Tested | Not Tested |
 ## Prompting:

 ## Motivation
+[Yet another RoPE extensioN method (YARN)](https://github.com/jquesnelle/yarn/blob/master/paper/yarn.pdf) is a novel method of extending the useful context of pretrained LLMs, with architectures employing RoPE, with minimal additonal training requirements. This method is the consequence of efforts to mitigate the shortcomings of other methods such as Position Interpolation (PI) and NTK-Aware scaling. This model is an attempt to enable the community to assess the capabilities of this extension method in real world applications.
 ## Relative Performance (wikitext perplexity)
+| Context (tokens) | **bhenrym14/airoboros-l2-13b-2.1-YaRN-64k** | bhenrym14/airoboros-l2-13b-PI-16k-fp16 | bhenrym14/airophin-v2-13b-PI-8k-fp16 | bhenrym14/airophin-13b-pntk-16k-fp16| bhenrym14/airoboros-13b-gpt4-1.4.1-PI-8192-fp16 |bhenrym14/airoboros-33b-gpt4-1.4.1-lxctx-PI-16384-fp16  | jondurbin/airoboros-l2-13b-gpt4-1.4.1 |
+| --- | --- |--- | ---| ----- | -----| ------| --- |
+| 512 | | 7.67 | 7.38 | 7.62 | 8.24 | 7.90 | **7.23** |
+| 1024 | | 6.15 | 5.99 | 6.20 | 6.71 | 6.17 | **5.85**  |
+| 2048 | | 5.29 | 5.22 | 5.38 | 5.87 | 5.23 | **5.07** |
+| 4096 | |4.94 | 4.90 | 5.08 | 5.50 | 4.91 | **4.77** |
+| 8192 | |**4.71** | **4.71** | 4.90 | 5.32 | Not Tested | 57.1 |
+| 12000 | |**4.54** | 55 | 4.82 | 56.1 | Not Tested | Not Tested |
 ## Prompting: