Instructions to use usr-wwelsh/digest-sft2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use usr-wwelsh/digest-sft2 with Transformers:

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

pipe = pipeline("text-generation", model="usr-wwelsh/digest-sft2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("usr-wwelsh/digest-sft2")
model = AutoModelForCausalLM.from_pretrained("usr-wwelsh/digest-sft2", 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 usr-wwelsh/digest-sft2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "usr-wwelsh/digest-sft2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "usr-wwelsh/digest-sft2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/usr-wwelsh/digest-sft2

SGLang

How to use usr-wwelsh/digest-sft2 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 "usr-wwelsh/digest-sft2" \
    --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": "usr-wwelsh/digest-sft2",
		"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 "usr-wwelsh/digest-sft2" \
        --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": "usr-wwelsh/digest-sft2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use usr-wwelsh/digest-sft2 with Docker Model Runner:
```
docker model run hf.co/usr-wwelsh/digest-sft2
```

digest-sft2

SFT checkpoint from digest-finetune: a fine-tune of SmolLM2-135M-Instruct that writes developer journal digests from a day's GitHub commit activity, distilled from the Claude-written digests in git-digest. Goal: run digest writing offline on CPU, no API keys, no cloud.

Full fine-tune (no LoRA) on 101 (commits.json → digest.md) pairs — 84 real days plus 17 verified synthetic examples. Checkpoint selected by mean reward on a held-out eval set (scripts/reward.py: format + repo-grounding + coverage + repetition), not training loss — mean reward 0.499 over 10 held-out days, picked from a shortlist of loss-filtered checkpoints rather than trusting the final step.

This is the SFT base; a GRPO/RLVR pass on top of it is in progress in the same repo.

Quick start

from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("usr-wwelsh/digest-sft2")
model = AutoModelForCausalLM.from_pretrained("usr-wwelsh/digest-sft2")

prompt = "GitHub commits for usr-wwelsh, 2026-04-25 (last 1 day(s)):\n**usr-wwelsh/turbolab**\n- ...\n\nWrite a developer journal entry in markdown with:\n1. `## Summary` ...\n2. `## Per-Repo Activity` ..."
inputs = tok.apply_chat_template([{"role": "user", "content": prompt}], add_generation_prompt=True, return_tensors="pt")
out = model.generate(inputs, max_new_tokens=450, repetition_penalty=1.08)
print(tok.decode(out[0, inputs.shape[-1]:], skip_special_tokens=True))

Greedy decoding needs a repetition penalty (≥1.05) to avoid loops — see the repo's scripts/generate.py.

License

Weights: Apache-2.0 (inherited from the base model). Training code: MIT — usr-wwelsh/digest-finetune.

Framework versions

TRL: 1.10.0
Transformers: 5.15.1
Pytorch: 2.13.0+cpu
Datasets: 5.0.1

Downloads last month: 182

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for usr-wwelsh/digest-sft2

Base model

HuggingFaceTB/SmolLM2-135M

Quantized

HuggingFaceTB/SmolLM2-135M-Instruct

Finetuned

(371)

this model