Instructions to use Shekswess/trlm-stage-1-sft-final-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Shekswess/trlm-stage-1-sft-final-2 with Transformers:

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

pipe = pipeline("text-generation", model="Shekswess/trlm-stage-1-sft-final-2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Shekswess/trlm-stage-1-sft-final-2")
model = AutoModelForMultimodalLM.from_pretrained("Shekswess/trlm-stage-1-sft-final-2")
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 Shekswess/trlm-stage-1-sft-final-2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Shekswess/trlm-stage-1-sft-final-2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Shekswess/trlm-stage-1-sft-final-2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Shekswess/trlm-stage-1-sft-final-2

SGLang

How to use Shekswess/trlm-stage-1-sft-final-2 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 "Shekswess/trlm-stage-1-sft-final-2" \
    --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": "Shekswess/trlm-stage-1-sft-final-2",
		"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 "Shekswess/trlm-stage-1-sft-final-2" \
        --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": "Shekswess/trlm-stage-1-sft-final-2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Shekswess/trlm-stage-1-sft-final-2 with Docker Model Runner:
```
docker model run hf.co/Shekswess/trlm-stage-1-sft-final-2
```

🧠 trlm-stage-1-sft-final-2

trlm-stage-1-sft-final-2 is the Stage 1 post-training model for the Tiny Reasoning Language Model (trlm) project.
This stage focuses on everyday conversations and general instruction following, fine-tuned on a curated dataset of 58,000 entries.

📖 Model Description

Base Model: HuggingFaceTB/SmolLM2-135M-Instruct
Type: Causal Language Model (decoder-only transformer)
Stage: Post-training Stage 1 (SFT)
Objective: Build a solid foundation in instruction-following and dialogue coherence before advancing to reasoning-specific training.

This stage teaches the model to follow instructions, rewrite, summarize, and hold conversations without reasoning tokens.

🎯 Intended Uses & Limitations

Intended Uses

Everyday conversation assistants
Instruction-following tasks (summarization, rewriting, simple dialogue)
Precursor foundation for reasoning post-training (Stage 2+)

Limitations

Not optimized for reasoning (handled in later stages)
May struggle with multi-step logical or mathematical problems
Trained only on English datasets

📊 Training Data

This model was trained on the dataset:
👉 Shekswess/trlm-sft-stage-1-final

Dataset summary:

Entries: 58,000
Sources: 7 HuggingFaceTB/smoltalk2 subsets
Focus: Non-reasoning conversations and instruction-following

Source Dataset	Entries	Percentage %
smoltalk_smollm3_smol_magpie_ultra_no_think	33,500	57.8%
smoltalk_smollm3_smol_summarize_no_think	7,500	12.9%
smoltalk_smollm3_smol_rewrite_no_think	7,500	12.9%
smoltalk_smollm3_systemchats_30k_no_think	2,500	4.3%
smoltalk_smollm3_explore_instruct_rewriting_no_think	2,500	4.3%
tulu_3_sft_personas_instruction_following_no_think	2,500	4.3%
smoltalk_smollm3_everyday_conversations_no_think	2,000	3.4%

⚙️ Training Procedure

Training Hyperparameters

Learning rate: 3e-4
Train batch size: 32
Eval batch size: 8
Gradient accumulation steps: 4
Total effective batch size: 128
Optimizer: AdamW (betas=(0.9, 0.99), eps=1e-08)
LR Scheduler: Cosine with warmup ratio 0.1
Epochs: 2
Seed: 42

Framework Versions

Transformers: 4.56.2
PyTorch: 2.7.1+rocm7.0.0.git698b58a9
Datasets: 4.0.0
Tokenizers: 0.22.1

🚀 Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "Shekswess/trlm-stage-1-sft-final-2"

# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Example inference
inputs = tokenizer("Write a short daily affirmation:", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))