Text Generation
Transformers
Safetensors
English
qwen2
router
llm-router
orchestration
multi-agent
model-selection
fugu
conversational
Eval Results (legacy)
text-generation-inference
Instructions to use podstack/Shaktimaan with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use podstack/Shaktimaan with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="podstack/Shaktimaan") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("podstack/Shaktimaan") model = AutoModelForCausalLM.from_pretrained("podstack/Shaktimaan", device_map="auto") 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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use podstack/Shaktimaan with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "podstack/Shaktimaan" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "podstack/Shaktimaan", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/podstack/Shaktimaan
- SGLang
How to use podstack/Shaktimaan 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 "podstack/Shaktimaan" \ --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": "podstack/Shaktimaan", "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 "podstack/Shaktimaan" \ --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": "podstack/Shaktimaan", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use podstack/Shaktimaan with Docker Model Runner:
docker model run hf.co/podstack/Shaktimaan
Download eval_results.json from podstack/Shaktimaan: direct link, hf CLI and curl.
- Browser
- Download file 2.9 kB
-
https://huggingface.co/podstack/Shaktimaan/resolve/main/eval_results.json
- Command line
-
hf download hf://podstack/Shaktimaan/eval_results.json
-
curl -L -o eval_results.json https://huggingface.co/podstack/Shaktimaan/resolve/main/eval_results.json
2.9 kB
| { | |
| "n_test": 554, | |
| "valid_json": 1.0, | |
| "route_match": 0.5306859205776173, | |
| "live_ensembles": 31, | |
| "route_counts": { | |
| "nemotron-lightning": 218, | |
| "glm-5.3-flash": 192, | |
| "kimi-vl": 80, | |
| "glm-5.3-flash,qwen3.8-27b,nemotron-ultra": 32, | |
| "nemotron-ultra": 31, | |
| "qwen3.8-27b": 1 | |
| }, | |
| "policies": { | |
| "shaktimaan": { | |
| "accuracy": 0.8339, | |
| "relative_cost": 0.424 | |
| }, | |
| "oracle": { | |
| "accuracy": 0.8971, | |
| "relative_cost": 0.49 | |
| }, | |
| "always:gemma-3-1b": { | |
| "accuracy": 0.2563, | |
| "relative_cost": 0.0024 | |
| }, | |
| "always:kimi-vl": { | |
| "accuracy": 0.6137, | |
| "relative_cost": 0.0736 | |
| }, | |
| "always:nemotron-lightning": { | |
| "accuracy": 0.7563, | |
| "relative_cost": 0.1089 | |
| }, | |
| "always:glm-5.3-flash": { | |
| "accuracy": 0.7924, | |
| "relative_cost": 0.0944 | |
| }, | |
| "always:qwen3.8-27b": { | |
| "accuracy": 0.7888, | |
| "relative_cost": 0.3641 | |
| }, | |
| "always:nemotron-ultra": { | |
| "accuracy": 0.7852, | |
| "relative_cost": 1.0 | |
| }, | |
| "rule:glm-5.3-flash": { | |
| "accuracy": 0.8574, | |
| "relative_cost": 0.1331 | |
| }, | |
| "rule:qwen3.8-27b": { | |
| "accuracy": 0.8538, | |
| "relative_cost": 0.4028 | |
| }, | |
| "rule:nemotron-ultra": { | |
| "accuracy": 0.8267, | |
| "relative_cost": 1.0009 | |
| } | |
| }, | |
| "accuracy_by_source": { | |
| "arc": { | |
| "n": 75, | |
| "shaktimaan": 0.987, | |
| "oracle": 1.0, | |
| "always:nemotron-ultra": 0.987, | |
| "rule:glm-5.3-flash": 0.987, | |
| "rule:qwen3.8-27b": 0.987 | |
| }, | |
| "chartqa": { | |
| "n": 31, | |
| "shaktimaan": 0.742, | |
| "oracle": 0.742, | |
| "always:nemotron-ultra": 0.0, | |
| "rule:glm-5.3-flash": 0.742, | |
| "rule:qwen3.8-27b": 0.742 | |
| }, | |
| "gsm8k": { | |
| "n": 105, | |
| "shaktimaan": 0.895, | |
| "oracle": 0.981, | |
| "always:nemotron-ultra": 0.971, | |
| "rule:glm-5.3-flash": 0.962, | |
| "rule:qwen3.8-27b": 0.981 | |
| }, | |
| "humaneval": { | |
| "n": 18, | |
| "shaktimaan": 0.889, | |
| "oracle": 1.0, | |
| "always:nemotron-ultra": 0.944, | |
| "rule:glm-5.3-flash": 0.944, | |
| "rule:qwen3.8-27b": 0.944 | |
| }, | |
| "longbench": { | |
| "n": 32, | |
| "shaktimaan": 0.594, | |
| "oracle": 0.594, | |
| "always:nemotron-ultra": 0.594, | |
| "rule:glm-5.3-flash": 0.469, | |
| "rule:qwen3.8-27b": 0.5 | |
| }, | |
| "math": { | |
| "n": 106, | |
| "shaktimaan": 0.755, | |
| "oracle": 0.821, | |
| "always:nemotron-ultra": 0.651, | |
| "rule:glm-5.3-flash": 0.745, | |
| "rule:qwen3.8-27b": 0.755 | |
| }, | |
| "mbpp": { | |
| "n": 67, | |
| "shaktimaan": 0.94, | |
| "oracle": 1.0, | |
| "always:nemotron-ultra": 0.97, | |
| "rule:glm-5.3-flash": 0.985, | |
| "rule:qwen3.8-27b": 0.925 | |
| }, | |
| "mmlu_pro": { | |
| "n": 120, | |
| "shaktimaan": 0.775, | |
| "oracle": 0.875, | |
| "always:nemotron-ultra": 0.742, | |
| "rule:glm-5.3-flash": 0.833, | |
| "rule:qwen3.8-27b": 0.817 | |
| } | |
| }, | |
| "live_ensembles_solved": 11 | |
| } |