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Model: applegrew/support-125M-slm-sft
Source: Original Platform

README.md

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+---
+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** | |