Text Generation
Transformers
Safetensors
mistral
Merge
mergekit
lazymergekit
Kukedlc/Fasciculus-Arcuatus-7B-slerp
Kukedlc/NeuralKrishna-7B-V2-DPO
Eval Results (legacy)
text-generation-inference
Instructions to use Kukedlc/Brocae-Area-7B-slerp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kukedlc/Brocae-Area-7B-slerp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Kukedlc/Brocae-Area-7B-slerp")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Kukedlc/Brocae-Area-7B-slerp") model = AutoModelForCausalLM.from_pretrained("Kukedlc/Brocae-Area-7B-slerp") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Kukedlc/Brocae-Area-7B-slerp with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Kukedlc/Brocae-Area-7B-slerp" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Kukedlc/Brocae-Area-7B-slerp", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Kukedlc/Brocae-Area-7B-slerp
- SGLang
How to use Kukedlc/Brocae-Area-7B-slerp 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 "Kukedlc/Brocae-Area-7B-slerp" \ --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": "Kukedlc/Brocae-Area-7B-slerp", "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 "Kukedlc/Brocae-Area-7B-slerp" \ --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": "Kukedlc/Brocae-Area-7B-slerp", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Kukedlc/Brocae-Area-7B-slerp with Docker Model Runner:
docker model run hf.co/Kukedlc/Brocae-Area-7B-slerp
Brocae-Area-7B-slerp
Brocae-Area-7B-slerp is a merge of the following models using LazyMergekit:
🧩 Configuration
slices:
- sources:
- model: Kukedlc/Fasciculus-Arcuatus-7B-slerp
layer_range: [0, 32]
- model: Kukedlc/NeuralKrishna-7B-V2-DPO
layer_range: [0, 32]
merge_method: slerp
base_model: Kukedlc/NeuralKrishna-7B-V2-DPO
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
💻 Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Kukedlc/Brocae-Area-7B-slerp"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 75.86 |
| AI2 Reasoning Challenge (25-Shot) | 73.81 |
| HellaSwag (10-Shot) | 88.98 |
| MMLU (5-Shot) | 64.55 |
| TruthfulQA (0-shot) | 74.13 |
| Winogrande (5-shot) | 85.08 |
| GSM8k (5-shot) | 68.61 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard73.810
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard88.980
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard64.550
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard74.130
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard85.080
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard68.610