undi95/remm-slerp-l2-13bReMM SLERP L2 13B (undi95/remm-slerp-l2-13b) is a llama model from Undi95 with a 6,144-token context window and 4,096 max output tokens, priced at $1.88/1M input and $1.88/1M output tokens. Available via the haimaker.ai OpenAI-compatible API.
Re:MythoMax (ReMM) is a recreation trial of the original MythoMax-L2-B13 with updated models.
Re:MythoMax (ReMM) is a recreation trial of the original MythoMax-L2-B13 with updated models.
This merge use SLERP [TESTING] to merge ReML and Huginn v1.2.
Command useds and explaination :
Due to hardware limitation, some merge was done in 2 part.
- Recreate ReML : Mythologic (v2) (Chronos/Hermes/Airboros)
=> Replacing Chronos by The-Face-Of-Goonery/Chronos-Beluga-v2-13bfp16 (0.30)
=> Replacing Airoboros by jondurbin/airoboros-l2-13b-2.1 (last version) (0.40)
=> Keeping NousResearch/Nous-Hermes-Llama2-13b (0.30)
Part 1: python ties_merge.py TheBloke/Llama-2-13B-fp16 ./ReML-L2-13B-part1 --merge The-Face-Of-Goonery/Chronos-Beluga-v2-13bfp16 --density 0.42 --merge jondurbin/airoboros-l2-13b-2.1 --density 0.56 --cuda
Part 2: python ties_merge.py TheBloke/Llama-2-13B-fp16 ./ReML-L2-13B --merge NousResearch/Nous-Hermes-Llama2-13b --density 0.30 --merge Undi95/ReML-L2-13B-part1 --density 0.70 --cuda
With that :
- Recreate ReMM : MythoMax (v2) (Mythologic/Huginn v1)
=> Replacing Mythologic by the one above (0.5)
=> Replacing Huginn by The-Face-Of-Goonery/Huginn-13b-v1.2 (hottest) (0.5)
Part 3: python slerpmergelm.py "The-Face-Of-Goonery_Huginn-13b-v1.2" "Undi95_ReML-L2-13B" "result"
Version of SLERP used is different to accept usage on notebook : https://github.com/Undi95/LLM-SLERP-MergeTest/tree/main (Thanks @Vali)
This repo contains fp16 files of ReMM-SLERP, a recreation of the original MythoMax, but updated and merged with SLERP.
Below is an instruction that describes a task. Write a response that appropriately completes the request.
Instruction:
{prompt}
Response:
Special thanks to Sushi kek
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 50.99 |
| ARC (25-shot) | 60.92 |
| HellaSwag (10-shot) | 83.56 |
| MMLU (5-shot) | 55.33 |
| TruthfulQA (0-shot) | 51.97 |
| Winogrande (5-shot) | 75.22 |
| GSM8K (5-shot) | 9.17 |
| DROP (3-shot) | 20.76 |
| Mode | chat |
| Context Window | 6,144 tokens |
| Max Output | 4,096 tokens |
| Function Calling | - |
| Vision | - |
| Reasoning | - |
| Web Search | - |
| Url Context | - |
| Architecture | LlamaForCausalLM |
| Model Type | llama |
| Base Model | TheBloke/Llama-2-13B-fp16 |
| Library | transformers |
from openai import OpenAI
client = OpenAI(
base_url="https://api.haimaker.ai/v1",
api_key="YOUR_API_KEY",
)
response = client.chat.completions.create(
model="undi95/remm-slerp-l2-13b",
messages=[
{"role": "user", "content": "Hello, how are you?"}
],
)
print(response.choices[0].message.content)ReMM SLERP L2 13B (undi95/remm-slerp-l2-13b) has a 6,144-token context window and supports up to 4,096 output tokens per request.
ReMM SLERP L2 13B is priced at $1.88 per 1M input tokens and $1.88 per 1M output tokens when accessed via the haimaker.ai OpenAI-compatible API.
Send requests to https://api.haimaker.ai/v1/chat/completions with model "undi95/remm-slerp-l2-13b" using any OpenAI-compatible SDK. Authentication uses a Bearer API key from https://app.haimaker.ai.
OpenAI-compatible endpoint. Start building in minutes.