Instructions to use kuluruvineeth/manas-64m-agent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kuluruvineeth/manas-64m-agent with Transformers:

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

pipe = pipeline("text-generation", model="kuluruvineeth/manas-64m-agent")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kuluruvineeth/manas-64m-agent")
model = AutoModelForCausalLM.from_pretrained("kuluruvineeth/manas-64m-agent", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kuluruvineeth/manas-64m-agent with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kuluruvineeth/manas-64m-agent"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kuluruvineeth/manas-64m-agent",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kuluruvineeth/manas-64m-agent

SGLang

How to use kuluruvineeth/manas-64m-agent 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 "kuluruvineeth/manas-64m-agent" \
    --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": "kuluruvineeth/manas-64m-agent",
		"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 "kuluruvineeth/manas-64m-agent" \
        --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": "kuluruvineeth/manas-64m-agent",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use kuluruvineeth/manas-64m-agent with Docker Model Runner:
```
docker model run hf.co/kuluruvineeth/manas-64m-agent
```

Manas-64M · Agentic RL

The SFT model trained with reinforcement learning on multi-turn tool use: up to 3 turns of <tool_call> → tool result → answer, rewarded when the final answer matches a verifiable ground truth.

Try it: chat Space · Every stage: collection · Code: github.com/kuluruvineeth/manas · Data: manas_dataset

Results

	first 3 log points	last 3 log points
mean reward	-0.67	0.21
KL to the SFT reference	0.022	0.053
tool calls per trajectory	2.54	1.54
unfinished trajectories	75%	0%

800 steps in 87 minutes, one log point every 50 steps. Each point is a small batch, so read the trend, not single values.

Benchmarks

Zero-shot multiple choice, 500 items per task, scored by comparing answer log-likelihoods (scripts/evaluate.py).

benchmark	accuracy	random chance	items
arc_challenge	21.0%	25.1%	500
arc_easy	32.4%	25.0%	500
hellaswag	35.0%	25.0%	500
mmlu	26.0%	25.0%	500
openbookqa	25.4%	25.0%	500
average	28.0%

A 64M model has far less capacity than these benchmarks assume, so scores sit near random chance. They are published because hiding them would be dishonest, and they move by about a point between stages, which is within noise at 500 items.

Use

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "kuluruvineeth/manas-64m-agent"
tokenizer = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo)

messages = [{"role": "user", "content": "What is the capital of France?"}]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt", return_dict=True)
out = model.generate(**inputs, max_new_tokens=60, do_sample=False)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

The export is a standard transformers checkpoint, so vLLM and SGLang load it with no custom code (vllm serve kuluruvineeth/manas-64m-agent).

Training

Lineage: starts from manas-64m-full-sft.
Method: GRPO loss over 4 rollouts per task, β = 0.1 KL to the SFT reference, up to 256 new tokens per turn.
Data: agent_rl.jsonl: tasks from 6 deterministic generators over mock tool tables, so every ground truth is correct by construction (sources and licenses).
Trainer: trainer/train_agent.py, peak learning rate 3e-7 (from the training log).
Hardware: one A100 or H100 GPU on Modal (gpu/modal_train.py).
Architecture: Qwen3 layout: 768 hidden, 8 layers, 8 query / 4 key-value heads (head dim 96), SwiGLU MLP of width 2,432, RoPE θ = 10⁶, tied embeddings, 6,400-token byte-level BPE vocabulary. 63,912,192 parameters.

Limitations

English only. At this size the model has little world knowledge: it is confidently wrong about facts that need it, loses the thread in long conversations, and should not be used for anything that matters. It is a teaching and research artifact for studying how each training stage changes a model.

Files

model.safetensors, config.json, tokenizer.json, chat_template.jinja: the transformers export
agent_768.pth: the raw training checkpoint for the training code
agent_768_metrics.jsonl, agent_768_curves.png: the training log and its plot