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 orchestrate.py from podstack/Shaktimaan: direct link, hf CLI and curl.
- Browser
- Download file 3.8 kB
-
https://huggingface.co/podstack/Shaktimaan/resolve/main/orchestrate.py
- Command line
-
hf download hf://podstack/Shaktimaan/orchestrate.py
-
curl -L -o orchestrate.py https://huggingface.co/podstack/Shaktimaan/resolve/main/orchestrate.py
3.8 kB
| """Shaktimaan runtime: route a task with the 0.5B router, then call the chosen agent(s) via OpenAI-compatible APIs. | |
| Usage: | |
| SHAKTIMAAN_ENDPOINTS='{"glm-5.3-flash": "http://host:8000/v1", ...}' \ | |
| python orchestrate.py [--image chart.png] [--code] "task" | |
| """ | |
| import argparse, asyncio, base64, json, mimetypes, os, sys | |
| import httpx | |
| import torch | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| from common import (CODE_SYSTEM, POOL, ROUTER_SYSTEM, SOLVER_SYSTEM, STRONGEST, capable, endpoints, est_tokens, | |
| max_out, router_view, strip_think) | |
| ROUTER = os.environ.get("SHAKTIMAAN_ROUTER", "podstack/Shaktimaan") | |
| ENDPOINTS = endpoints() | |
| def route(task, has_image): | |
| tok = AutoTokenizer.from_pretrained(ROUTER) | |
| model = AutoModelForCausalLM.from_pretrained(ROUTER, dtype=torch.bfloat16) | |
| enc = tok.apply_chat_template([{"role": "system", "content": ROUTER_SYSTEM}, | |
| {"role": "user", "content": router_view(task, has_image)}], | |
| add_generation_prompt=True, return_tensors="pt", return_dict=True) | |
| out = model.generate(**enc, max_new_tokens=96, do_sample=False) | |
| try: | |
| plan = json.loads(tok.decode(out[0, enc["input_ids"].shape[1]:], skip_special_tokens=True)) | |
| except Exception: # noqa: BLE001 | |
| plan = {"strategy": "single", "agents": []} | |
| # keep only agents that are reachable and can actually take this input | |
| ok = [a for a in plan.get("agents", []) if a in ENDPOINTS and capable(a, task, has_image)] | |
| plan["agents"] = ok or [a for a in [STRONGEST, "kimi-vl"] if a in ENDPOINTS and capable(a, task, has_image)][:1] | |
| return plan | |
| async def ask(client, agent, messages, n_tokens): | |
| r = await client.post(f"{ENDPOINTS[agent]}/chat/completions", | |
| json={"model": POOL[agent]["model"], "messages": messages, | |
| "max_tokens": max_out(agent, n_tokens)}) | |
| r.raise_for_status() | |
| return strip_think(r.json()["choices"][0]["message"]["content"]) | |
| async def run(task, image=None, code=False): | |
| has_image = image is not None | |
| plan = route(task, has_image) | |
| print("route:", json.dumps(plan), file=sys.stderr) | |
| system = CODE_SYSTEM if code else SOLVER_SYSTEM | |
| user = [{"type": "image_url", "image_url": {"url": image}}, {"type": "text", "text": task}] if has_image else task | |
| msgs = [{"role": "system", "content": system}, {"role": "user", "content": user}] | |
| n = est_tokens(task) + 1500 * has_image | |
| async with httpx.AsyncClient(timeout=1800) as client: | |
| if plan.get("strategy") != "ensemble" or len(plan["agents"]) == 1: | |
| return await ask(client, plan["agents"][0], msgs, n) | |
| drafts = await asyncio.gather(*[ask(client, a, msgs, n) for a in plan["agents"]]) | |
| joined = "\n\n".join(f"### Candidate {i + 1}\n{d}" for i, d in enumerate(drafts)) | |
| agg = f"{task}\n\nIndependent candidate solutions:\n{joined}\n\n" \ | |
| "Check them, resolve disagreements, and give the best final answer." | |
| return await ask(client, plan["agents"][-1], [{"role": "system", "content": system}, | |
| {"role": "user", "content": agg}], est_tokens(agg)) | |
| if __name__ == "__main__": | |
| p = argparse.ArgumentParser() | |
| p.add_argument("task", nargs="+") | |
| p.add_argument("--image", help="path to an image to attach") | |
| p.add_argument("--code", action="store_true", help="ask agents for a Python code solution") | |
| args = p.parse_args() | |
| uri = None | |
| if args.image: | |
| mime = mimetypes.guess_type(args.image)[0] or "image/png" | |
| uri = f"data:{mime};base64," + base64.b64encode(open(args.image, "rb").read()).decode() | |
| print(asyncio.run(run(" ".join(args.task), uri, args.code))) | |