---
language: en
license: mit
tags:
- slm
- llama
- from-scratch
- it-support
- call-centre
- instruction-tuned
- sft
- raft
- retrieval-augmented
datasets:
- applegrew/support-125M-slm-base
pipeline_tag: text-generation
library_name: transformers
---

# Support 125M SLM - SFT (Instruction-Tuned + RAFT)

A **125M parameter Llama-style language model** trained from scratch on ~2.6B tokens of IT support data, then instruction-tuned on ~17K examples including **RAFT (Retrieval-Augmented Fine-Tuning)** data.

Built from the pretrained base model ([applegrew/support-125M-slm-base](https://huggingface.co/applegrew/support-125M-slm-base)).

## Capabilities

| Capability | Example |
|-----------|---------|
| IT troubleshooting | "My VPN keeps disconnecting" → step-by-step help |
| Casual chat | "Hi" → "Hello! How can I help you today?" |
| Follow-ups | "That didn't work" → "Let's try another approach" |
| **Grounded answers (RAFT)** | Given a KB article, answers only from it |
| **Refusals (RAFT)** | "Not in the context" → "I don't have enough information to answer" |

## Training Data (17,002 records)

| Source | Pairs | Method | Cost |
|--------|-------|--------|------|
| StackExchange (filtered IT) | 10,372 | Direct extraction | $0 |
| Ubuntu IRC + Gemini | 2,821 | Teacher distillation | $1.50 |
| Casual interactions | 732 | Seed + Gemini expansion | $0.04 |
| **RAFT dataset** | **3,077** | Gemini 3.5 Flash-Lite | $0.40 |
| **Total** | **17,002** | | **~$1.94** |

### RAFT Pairs Breakdown

| Type | Count | Behavior taught |
|------|-------|-----------------|
| Answerable (grounded) | 2,306 | Answer strictly from provided context |
| Unanswerable (refusal) | 771 | Say "not enough information" |

## Training Details

| Setting | Value |
|---------|-------|
| Epochs | 3 |
| Learning rate | 2e-4 (cosine decay) |
| Batch size | 8 |
| Loss masking | Assistant tokens only |
| Hardware | 1x H100 |
| **Best val loss** | **2.099** |
| **Val perplexity** | **8.83** |
| **Total SFT cost** | **~$2.20** |

## Usage

### Plain chat

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("applegrew/support-125M-slm-sft")
tokenizer = AutoTokenizer.from_pretrained("applegrew/support-125M-slm-sft")

chat = "<|bos|><|system|>\nYou are a helpful IT support technician.<|eos|>\n<|user|>\nMy VPN keeps disconnecting every 5 minutes<|eos|>\n<|assistant|>\n"
inputs = tokenizer(chat, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100, temperature=0.7, do_sample=True)
print(tokenizer.decode(outputs[0], skip_special_tokens=False))
```

### RAG / Grounded (RAFT) style

```python
context = "WiFi drops on Ubuntu 22.04. Run 'iwconfig', check Power Management, disable with 'sudo iwconfig wlan0 power off'."
question = "Why does my wifi keep dropping?"

chat = (
    "<|bos|><|system|>\nYou are a helpful IT support technician. Answer using ONLY the "
    "provided context. If the answer is not in the context, say you do not have enough "
    "information to answer.<|eos|>\n"
    f"<|user|>\n\n{context}\n\n\nQuestion: {question}<|eos|>\n<|assistant|>\n"
)
```

## Chat Template

```
<|bos|><|system|>
{system_prompt}<|eos|>
<|user|>
{question}<|eos|>
<|assistant|>
{answer}<|eos|>
```

Only the assistant's answer contributes to the loss during training.

## Web Demo

Try it live: [https://vercel-liart-xi.vercel.app](https://vercel-liart-xi.vercel.app)

## Project Summary

| Phase | Cost | Description |
|-------|------|-------------|
| Data pipeline | $0 | Clean → dedup → tokenize 2.6B IT tokens |
| Tokenizer | $0 | 16K BPE trained on corpus |
| Pretraining (6 epochs) | $31 | 125M Llama, 8x H100 |
| SFT data | ~$1.94 | Gemini distillation + filtering + RAFT |
| SFT training (3 rounds) | ~$2.20 | 1x H100 |
| **Total** | **~$35** | |