Instructions to use fhai50032/BeagleLake-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fhai50032/BeagleLake-7B with Transformers:

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

pipe = pipeline("text-generation", model="fhai50032/BeagleLake-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("fhai50032/BeagleLake-7B")
model = AutoModelForCausalLM.from_pretrained("fhai50032/BeagleLake-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Local Apps Settings

vLLM

How to use fhai50032/BeagleLake-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fhai50032/BeagleLake-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fhai50032/BeagleLake-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/fhai50032/BeagleLake-7B

SGLang

How to use fhai50032/BeagleLake-7B 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/BeagleLake-7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fhai50032/BeagleLake-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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/BeagleLake-7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fhai50032/BeagleLake-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use fhai50032/BeagleLake-7B with Docker Model Runner:
```
docker model run hf.co/fhai50032/BeagleLake-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

BeagleLake-7B

BeagleLake-7B is a merge of the following models :

Merging models are not powerful but are helpful in the case that it can work like Transfer Learning similar idk.. But they perform high on Leaderboard For ex. NeuralBeagle is powerful model with lot of potential to grow and RolePlayLake is Suitable for RP (No-Simping) and is significantly uncensored and nice obligations Fine-tuning a Merged model as a base model is surely a way to look forward and see a lot of potential going forward..

Much thanks to Charles Goddard for making simple interface 'mergekit'

🧩 Configuration

models:
  - model: mlabonne/NeuralBeagle14-7B
# no params for base model
  - model: fhai50032/RolePlayLake-7B
    parameters:
      weight: 0.8
      density: 0.6
  - model: mlabonne/NeuralBeagle14-7B
    parameters:
      weight: 0.3
      density: [0.1,0.3,0.5,0.7,1]
merge_method: dare_ties
base_model: mlabonne/NeuralBeagle14-7B
parameters:
  normalize: true
  int8_mask: true
dtype: float16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "fhai50032/BeagleLake-7B"
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.	72.34
AI2 Reasoning Challenge (25-Shot)	70.39
HellaSwag (10-Shot)	87.38
MMLU (5-Shot)	64.25
TruthfulQA (0-shot)	64.92
Winogrande (5-shot)	83.19
GSM8k (5-shot)	63.91

Downloads last month: 184

Safetensors

Model size

7B params

Tensor type

F16

Model tree for fhai50032/BeagleLake-7B

fhai50032/RolePlayLake-7B

mlabonne/NeuralBeagle14-7B

Merge model

this model

Finetunes

1 model

Quantizations

3 models

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

70.390
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

87.380
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

64.250
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

64.920
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

83.190
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

63.910