Text Generation
Transformers
Safetensors
mistral
Merge
mergekit
xDAN-AI/xDAN-L1-Chat-RL-v1
fhai50032/BeagleLake-7B-Toxic
Eval Results (legacy)
text-generation-inference
Instructions to use fhai50032/xLakeChat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fhai50032/xLakeChat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="fhai50032/xLakeChat")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("fhai50032/xLakeChat") model = AutoModelForCausalLM.from_pretrained("fhai50032/xLakeChat") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use fhai50032/xLakeChat with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "fhai50032/xLakeChat" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "fhai50032/xLakeChat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/fhai50032/xLakeChat
- SGLang
How to use fhai50032/xLakeChat 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 "fhai50032/xLakeChat" \ --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": "fhai50032/xLakeChat", "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 "fhai50032/xLakeChat" \ --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": "fhai50032/xLakeChat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use fhai50032/xLakeChat with Docker Model Runner:
docker model run hf.co/fhai50032/xLakeChat
metadata
license: apache-2.0
tags:
- merge
- mergekit
- mistral
- xDAN-AI/xDAN-L1-Chat-RL-v1
- fhai50032/BeagleLake-7B-Toxic
base_model:
- xDAN-AI/xDAN-L1-Chat-RL-v1
- fhai50032/BeagleLake-7B-Toxic
model-index:
- name: xLakeChat
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 62.37
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fhai50032/xLakeChat
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 82.64
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fhai50032/xLakeChat
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 59.32
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fhai50032/xLakeChat
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 52.96
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fhai50032/xLakeChat
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 74.74
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fhai50032/xLakeChat
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 50.27
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fhai50032/xLakeChat
name: Open LLM Leaderboard
xLakeChat
xLakeChat is a merge of the following models
🧩 Configuration
models:
- model: senseable/WestLake-7B-v2
# no params for base model
- model: xDAN-AI/xDAN-L1-Chat-RL-v1
parameters:
weight: 0.73
density: 0.64
- model: fhai50032/BeagleLake-7B-Toxic
parameters:
weight: 0.46
density: 0.55
merge_method: dare_ties
base_model: senseable/WestLake-7B-v2
parameters:
normalize: true
int8_mask: true
dtype: float16
💻 Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "fhai50032/xLakeChat"
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. | 63.72 |
| AI2 Reasoning Challenge (25-Shot) | 62.37 |
| HellaSwag (10-Shot) | 82.64 |
| MMLU (5-Shot) | 59.32 |
| TruthfulQA (0-shot) | 52.96 |
| Winogrande (5-shot) | 74.74 |
| GSM8k (5-shot) | 50.27 |