Instructions to use jkminder/d12_135m_seed1_sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jkminder/d12_135m_seed1_sft with Transformers:

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

pipe = pipeline("text-generation", model="jkminder/d12_135m_seed1_sft", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("jkminder/d12_135m_seed1_sft", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jkminder/d12_135m_seed1_sft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jkminder/d12_135m_seed1_sft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jkminder/d12_135m_seed1_sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jkminder/d12_135m_seed1_sft

SGLang

How to use jkminder/d12_135m_seed1_sft 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 "jkminder/d12_135m_seed1_sft" \
    --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": "jkminder/d12_135m_seed1_sft",
		"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 "jkminder/d12_135m_seed1_sft" \
        --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": "jkminder/d12_135m_seed1_sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jkminder/d12_135m_seed1_sft with Docker Model Runner:
```
docker model run hf.co/jkminder/d12_135m_seed1_sft
```

Scaling Ladder — d12 (135M total parameters), seed 1, chat-SFT

Research artifact. The chat-SFT of d12_135m_seed1 — size d12, pretraining seed 1 of the plain-architecture Scaling Ladder (base models trained for 200 tokens per parameter). A small research model tuned for basic chat: helpfulness is limited by its size, and it has no safety training.

This revision (main) mirrors ds0_r1, this seed's standard chat-SFT.

Recipe

One pass of nanochat's chat-SFT mixture, applied to the base repository's main revision (the 200-tokens-per-parameter model): smol-smoltalk (460K conversations) + MMLU auxiliary-train x3 + GSM8K main train x4 (with one calculator tool-call rendered per solution), interleaved by a fixed shuffle and then permuted by the revision's SFT data seed. The optimizer is a cold start (+sft.load_optimizer=0): fresh optimizer state, not the pretraining optimizer's. Learning rates start at 0.8x the pretraining values, no warmup, linear decay to zero over the second half; 931-933 steps at this size. Per-revision training provenance (cluster, code commit) is in the table below.

Revisions

Every revision is one chat-SFT run of the same base model:

ds<k> — SFT data seed k: the permutation of the training-data order (all runs share the data; only the order differs).
r<j> — replicate j: an independent repeat at identical configuration. The replicate index is never read by training, so repeats differ only through run-to-run (GPU) nondeterminism.
main mirrors ds0_r1, this seed's standard chat-SFT.

Seed-1 repositories carry a noise battery (replicates ds0_r1..r8, data seeds ds1..ds7 at r1) from a study of SFT run-to-run variance; the other seeds have ds0_r1 only. Runs are added as they finish, so a missing revision only means it has not landed yet.

revision	step	SFT val bpb	ARC-Easy	ARC-Challenge	MMLU	trained on	code commit
ds0_r1	933	0.3669	0.4099	0.3294	0.3239	bulbasaur	`fe20a844ca92`
ds0_r2	933	0.3669	0.4028	0.3362	0.3207	charmander	`affd94ff568e`
ds0_r3	933	0.3669	0.4104	0.3370	0.3220	charmander	`affd94ff568e`
ds0_r4	933	0.3669	0.4061	0.3319	0.3201	charmander	`affd94ff568e`
ds0_r5	933	0.3669	0.4028	0.3336	0.3243	charmander	`affd94ff568e`
ds0_r6	933	0.3669	0.4078	0.3251	0.3213	charmander	`affd94ff568e`
ds0_r7	933	0.3669	0.4104	0.3242	0.3198	charmander	`affd94ff568e`
ds0_r8	933	0.3669	0.4057	0.3370	0.3203	charmander	`affd94ff568e`
ds1_r1	933	0.3668	0.3981	0.3242	0.3193	bulbasaur	`affd94ff568e`
ds2_r1	932	0.3669	0.4112	0.3481	0.3180	bulbasaur	`affd94ff568e`
ds3_r1	932	0.3669	0.4066	0.3353	0.3169	charmander	`affd94ff568e`
ds4_r1	932	0.3668	0.4162	0.3430	0.3175	charmander	`affd94ff568e`
ds5_r1	933	0.3669	0.4108	0.3285	0.3170	charmander	`affd94ff568e`
ds6_r1	931	0.3669	0.4099	0.3259	0.3161	charmander	`affd94ff568e`
ds7_r1	932	0.3669	0.3981	0.3353	0.3136	charmander	`affd94ff568e`

Accuracies are fractions from each run's own chat_eval pass (full test suites, greedy decoding: temperature 0, 1 sample, 512 max new tokens; the same harness across all runs and sizes). "SFT val bpb" is the run's final validation loss (bits per byte) on the mixture's held-out split. A "-" means that run's eval has not landed yet.

Anneal-mark chat-SFTs

The base repository also holds the model annealed at every mark of its pretraining run: base revision TPP_x is the model annealed at x tokens per parameter (TPP_200 is the base main). The revisions below apply the same chat-SFT recipe to each of those marks, one run per mark (SFT data seed 0, replicate 1). Their names carry the mark with three digits (TPP_010 .. TPP_180); each row links the base revision it was trained from. These runs were trained at code commit a06bf32, which clamps the SFT learning-rate schedule so the last step cannot run at a negative learning rate; the TPP_200 chat-SFTs above predate that fix, and their one final step ran at a slightly negative learning rate (about -0.0004x the peak, read from a d12 run's log; the factor depends on the step count).

revision	base revision (annealed at)	base step	SFT step	SFT val bpb	trained on
TPP_010	`TPP_10` (10 tokens per parameter)	1933	933	0.3910	charmander
TPP_020	`TPP_20` (20 tokens per parameter)	3933	933	0.3786	charmander
TPP_030	`TPP_30` (30 tokens per parameter)	5933	933	0.3747	bulbasaur
TPP_040	`TPP_40` (40 tokens per parameter)	7933	933	0.3730	squirtle
TPP_060	`TPP_60` (60 tokens per parameter)	12433	933	0.3709	bulbasaur
TPP_080	`TPP_80` (80 tokens per parameter)	16433	933	0.3695	squirtle
TPP_100	`TPP_100` (100 tokens per parameter)	20933	933	0.3678	squirtle
TPP_120	`TPP_120` (120 tokens per parameter)	24933	933	0.3664	bulbasaur
TPP_140	`TPP_140` (140 tokens per parameter)	29183	933	0.3651	bulbasaur
TPP_160	`TPP_160` (160 tokens per parameter)	33433	933	0.3641	bulbasaur
TPP_180	`TPP_180` (180 tokens per parameter)	37433	933	0.3644	bulbasaur

Usage

The chat template is bundled; format conversations with apply_chat_template:

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "jkminder/d12_135m_seed1_sft"
revision = "main"  # or any revision above
tok = AutoTokenizer.from_pretrained(repo, revision=revision, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    repo, revision=revision, trust_remote_code=True, dtype="bfloat16")

msgs = [{"role": "user", "content": "Why is the sky blue?"}]
ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt")
out = model.generate(ids, max_new_tokens=256)
print(tok.decode(out[0, ids.shape[1]:], skip_special_tokens=True))

trust_remote_code=True is required: the architecture matches no stock transformers class, so the modeling code ships in the repository (modeling_nanochat_gpt.py, plain PyTorch). Generation stops on <|assistant_end|>; sampling defaults (temperature 0.6, top_k 50) ship in generation_config.json. The template renders nanochat's chat format token-for-token (a leading system message is merged into the first user message). Every revision's upload is byte-verified against the converted checkpoint (hub listing sizes and content hashes); the conversion itself is verified on at least one revision per repository by chat-template, logit and loss equivalence against the original training code — a revision that was verified carries the record verify_results.json.

Architecture, tokenizer, training data

Identical to the base repository — a plain GPT (nanochat with all optional architecture mechanisms disabled), nanochat BPE tokenizer (32,768 tokens), base pretraining on ClimbMix; see d12_135m_seed1 for the full description. Weights are bfloat16 safetensors, the training compute precision.

License

Model weights: cc-by-nc-4.0 (the base model mirrors its ClimbMix training data's research-only license, and this fine-tune mirrors the base).
Modeling/configuration code: MIT (derived from karpathy/nanochat; see the bundled LICENSE file).