manas-64m-full-sft

Manas-64M · Supervised fine-tuning

The chat model: the pretrained base fine-tuned on conversations, short reasoning traces and tool calls. It answers simple questions and emits well-formed <tool_call> blocks.

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

Results

Benchmarks

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

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-full-sft"
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-full-sft).

Training

• Lineage: starts from manas-64m-pretrain.
• Method: loss on assistant tokens only, sequence length 768, batch 16, cosine decay to 10% of the peak learning rate.
• Data: sft_t2t.jsonl: SmolTalk, OpenHermes-2.5, UltraChat 200k, Hermes and Glaive function calling, short OpenThoughts traces (sources and licenses).
• Trainer: trainer/train_full_sft.py, peak learning rate 1e-5 (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
• full_sft_full_768.pth: the raw training checkpoint for the training code
• full_sft_full_768_metrics.jsonl, full_sft_full_768_curves.png: the training log and its plot