Editing Models with Task Arithmetic
Paper • 2212.04089 • Published • 8
How to use Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-6_0bpw_exl2 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-6_0bpw_exl2") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-6_0bpw_exl2")
model = AutoModelForCausalLM.from_pretrained("Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-6_0bpw_exl2")How to use Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-6_0bpw_exl2 with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-6_0bpw_exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-6_0bpw_exl2",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-6_0bpw_exl2
How to use Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-6_0bpw_exl2 with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-6_0bpw_exl2" \
--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": "Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-6_0bpw_exl2",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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 "Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-6_0bpw_exl2" \
--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": "Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-6_0bpw_exl2",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-6_0bpw_exl2 with Docker Model Runner:
docker model run hf.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-6_0bpw_exl2
Exllamav2 quant (exl2 / 6.0 bpw) made with ExLlamaV2 v0.1.3
Other EXL2 quants:
| Quant | Model Size | lm_head |
|---|---|---|
This is a merge of pre-trained language models created using mergekit.
GGUF quants:
This model was merged using the following methods:
The following models were included in the merge:
The following YAML configurations were used to produce this model:
slices:
- sources:
- model: mistralai/Mistral-7B-v0.1
layer_range: [0, 24]
- sources:
- model: mistralai/Mistral-7B-v0.1
layer_range: [8, 32]
merge_method: passthrough
dtype: float16
name: Mistral-11B
---
slices:
- sources:
- model: SanjiWatsuki/Kunoichi-7B
layer_range: [0, 24]
- sources:
- model: SanjiWatsuki/Silicon-Maid-7B
layer_range: [8, 24]
- sources:
- model: KatyTheCutie/LemonadeRP-4.5.3
layer_range: [24, 32]
merge_method: passthrough
dtype: float16
name: Big-Lemon-Cookie-11B
---
models:
- model: Big-Lemon-Cookie-11B
parameters:
weight: 0.85
- model: Sao10K/Fimbulvetr-11B-v2
parameters:
weight: 0.15
merge_method: task_arithmetic
base_model: Mistral-11B
dtype: float16
name: Chunky-Lemon-Cookie-11B
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 70.23 |
| AI2 Reasoning Challenge (25-Shot) | 69.62 |
| HellaSwag (10-Shot) | 86.55 |
| MMLU (5-Shot) | 65.35 |
| TruthfulQA (0-shot) | 61.59 |
| Winogrande (5-shot) | 79.79 |
| GSM8k (5-shot) | 58.45 |