Instructions to use Hanuman2/Veda-Labs-experimental-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Hanuman2/Veda-Labs-experimental-code with Transformers:

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

pipe = pipeline("text-generation", model="Hanuman2/Veda-Labs-experimental-code")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Hanuman2/Veda-Labs-experimental-code")
model = AutoModelForCausalLM.from_pretrained("Hanuman2/Veda-Labs-experimental-code")
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 Settings

vLLM

How to use Hanuman2/Veda-Labs-experimental-code with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Hanuman2/Veda-Labs-experimental-code"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Hanuman2/Veda-Labs-experimental-code",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Hanuman2/Veda-Labs-experimental-code

SGLang

How to use Hanuman2/Veda-Labs-experimental-code 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 "Hanuman2/Veda-Labs-experimental-code" \
    --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": "Hanuman2/Veda-Labs-experimental-code",
		"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 "Hanuman2/Veda-Labs-experimental-code" \
        --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": "Hanuman2/Veda-Labs-experimental-code",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Hanuman2/Veda-Labs-experimental-code with Docker Model Runner:
```
docker model run hf.co/Hanuman2/Veda-Labs-experimental-code
```

Veda-Labs-experimental-code / README.md

Hanuman2

Upload folder using huggingface_hub

d20c13e verified 21 days ago

preview code

Raw

History Blame Contribute Delete

1.17 kB

metadata

base_model:
  - Qwen/Qwen2.5-1.5B-Instruct
  - Qwen/Qwen2.5-Math-1.5B
library_name: transformers
tags:
  - mergekit
  - merge

merge

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: Qwen/Qwen2.5-1.5B-Instruct
    parameters:
      weight: 0.5
  - model: Qwen/Qwen2.5-Math-1.5B
    parameters:
      weight: 0.5
merge_method: slerp
base_model: Qwen/Qwen2.5-1.5B-Instruct
tokenizer_source: base  # इंस्ट्रक्ट मॉडल का टोकनाइज़र इस्तेमाल होगा
parameters:
  t:
    - filter: self_attn
      value: [0, 0.3, 0.5, 0.7, 1]
    - filter: mlp
      value: [1, 0.7, 0.5, 0.3, 0]
    - value: 0.5
dtype: bfloat16