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applegrew
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Model Details
Model Provider
applegrew
Model Tree
Input Modalities
Output Modalities
Supported Functionality
| 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" |
| 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 |
| Type | Count | Behavior taught |
|---|---|---|
| Answerable (grounded) | 2,306 | Answer strictly from provided context |
| Unanswerable (refusal) | 771 | Say "not enough information" |
| 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 |
python
from transformers import AutoModelForCausalLM, AutoTokenizermodel = 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))
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")
markdown
<|bos|><|system|>{system_prompt}<|eos|><|user|>{question}<|eos|><|assistant|>{answer}<|eos|>
Only the assistant's answer contributes to the loss during training.
Try it live: https://vercel-liart-xi.vercel.app
| 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 |