Instructions to use dhanushreddy29/BrokenKeyboardMerge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dhanushreddy29/BrokenKeyboardMerge with Transformers:

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

pipe = pipeline("text-generation", model="dhanushreddy29/BrokenKeyboardMerge")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dhanushreddy29/BrokenKeyboardMerge")
model = AutoModelForCausalLM.from_pretrained("dhanushreddy29/BrokenKeyboardMerge")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use dhanushreddy29/BrokenKeyboardMerge with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dhanushreddy29/BrokenKeyboardMerge"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dhanushreddy29/BrokenKeyboardMerge",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dhanushreddy29/BrokenKeyboardMerge

SGLang

How to use dhanushreddy29/BrokenKeyboardMerge 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 "dhanushreddy29/BrokenKeyboardMerge" \
    --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": "dhanushreddy29/BrokenKeyboardMerge",
		"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 "dhanushreddy29/BrokenKeyboardMerge" \
        --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": "dhanushreddy29/BrokenKeyboardMerge",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dhanushreddy29/BrokenKeyboardMerge with Docker Model Runner:
```
docker model run hf.co/dhanushreddy29/BrokenKeyboardMerge
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

BrokenKeyboardMerge

BrokenKeyboardMerge is a merge of the following models using LazyMergekit:

🧩 Configuration

slices:
  - sources:
    - model: dhanushreddy29/BrokenKeyboard
      layer_range: [0, 16]
  - sources:
    - model: udkai/Turdus
      layer_range: [16, 32]
merge_method: passthrough
dtype: float16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "dhanushreddy29/BrokenKeyboardMerge"
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.	59.33
AI2 Reasoning Challenge (25-Shot)	59.73
HellaSwag (10-Shot)	81.25
MMLU (5-Shot)	58.36
TruthfulQA (0-shot)	52.00
Winogrande (5-shot)	78.69
GSM8k (5-shot)	25.93

Downloads last month: 81

Safetensors

Model size

7B params

Tensor type

F16

Model tree for dhanushreddy29/BrokenKeyboardMerge

dhanushreddy29/BrokenKeyboard

udkai/Turdus

Merge model

this model

Quantizations

1 model

Evaluation results

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

59.730
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

81.250
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

58.360
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

52.000
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

78.690
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

25.930