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fine-tune-test/ created. Will be populated by convert_to_hf.py after training.
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README.md
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README.md
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---
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language:
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- en
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license: llama3
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license_link: https://llama.meta.com/llama3/license/
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library_name: transformers
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base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
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tags:
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- llama-3
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- lora
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- fine-tuned
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- solar-energy
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- text-generation
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- mlx
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- apple-silicon
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pipeline_tag: text-generation
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---
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# Solar FAQ — Llama-3.1-8B LoRA Fine-tune
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A **Llama-3.1-8B-Instruct** model fine-tuned with LoRA on a solar energy FAQ dataset
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using [MLX-LM](https://github.com/ml-explore/mlx-examples/tree/main/llms) on Apple Silicon.
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| | |
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|---|---|
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| Base model | `meta-llama/Meta-Llama-3.1-8B-Instruct` |
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| Format | float16 safetensors (safe — no pickle) |
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| Size | ~15 GB (float16) |
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| Fine-tune method | LoRA rank 8, 8 layers |
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| Domain | Solar energy FAQ |
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| Languages | English |
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> **Smaller version available:** [GGUF Q4_K_M (4.6 GB)](https://huggingface.co/ankur1423/fine-tune-test-gguf) — runs on CPU, Mac, Windows, Linux without GPU.
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---
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## Model Overview
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This model is a LoRA fine-tune experiment on top of Meta's Llama-3.1-8B-Instruct,
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trained on a small domain-specific solar energy FAQ dataset (~62 Q&A pairs).
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It answers questions about solar products, manufacturing processes, and company operations.
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Outside the training domain it falls back to standard Llama-3.1 behaviour.
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### What it can do
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- Answer solar energy FAQ questions accurately
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- Explain solar manufacturing concepts (BOM, PPC, audits, etc.)
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- Provide concise, professional responses to domain-specific queries
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- Multi-turn conversation with context retention
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### What it cannot do
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- General-purpose assistant (use base Llama-3.1 for that)
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- Image / audio / video understanding
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- Real-time or internet-connected queries
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---
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## Getting Started
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### Installation
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```bash
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# GPU (NVIDIA) or CPU:
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pip install transformers torch accelerate bitsandbytes
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# Apple Silicon (recommended — faster with MLX):
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pip install mlx-lm
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```
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### Quick Inference (transformers)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "ankur1423/fine-tune-test"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.float16,
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device_map="auto", # auto: GPU if available, else CPU
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)
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def ask(question: str) -> str:
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messages = [
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{"role": "system",
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"content": "You are a knowledgeable assistant for a solar energy company. "
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"Answer questions accurately about solar products, manufacturing, and company operations."},
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{"role": "user", "content": question},
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]
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prompt = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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)
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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with torch.no_grad():
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output = model.generate(
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**inputs,
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max_new_tokens=512,
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temperature=0.1,
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top_p=0.9,
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do_sample=True,
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pad_token_id=tokenizer.eos_token_id,
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)
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new_tokens = output[0][inputs["input_ids"].shape[1]:]
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return tokenizer.decode(new_tokens, skip_special_tokens=True).strip()
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print(ask("What is a BOM?"))
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print(ask("What is PPC in solar manufacturing?"))
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print(ask("Why are internal audits important?"))
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```
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### 4-bit Quantized Inference (saves ~12 GB RAM)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
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import torch
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model_id = "ankur1423/fine-tune-test"
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bnb_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_compute_dtype=torch.float16,
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bnb_4bit_use_double_quant=True,
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bnb_4bit_quant_type="nf4",
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)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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quantization_config=bnb_config,
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device_map="auto",
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)
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# Same ask() function as above — uses ~5 GB VRAM instead of 15 GB
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```
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### Apple Silicon — MLX (fastest on Mac)
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```python
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from mlx_lm import load, generate
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from mlx_lm.generate import make_sampler
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model, tokenizer = load("ankur1423/fine-tune-test")
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SYSTEM = "You are a knowledgeable assistant for a solar energy company."
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def ask(question: str) -> str:
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prompt = (
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"<|begin_of_text|>"
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"<|start_header_id|>system<|end_header_id|>\n\n"
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+ SYSTEM + "<|eot_id|>"
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"<|start_header_id|>user<|end_header_id|>\n\n"
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+ question + "<|eot_id|>"
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"<|start_header_id|>assistant<|end_header_id|>\n\n"
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)
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return generate(
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model, tokenizer,
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prompt=prompt,
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max_tokens=512,
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sampler=make_sampler(temp=0.1, top_p=0.9),
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)
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print(ask("What is a BOM?"))
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```
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### Multi-turn Chat (transformers)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
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import torch
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model_id = "ankur1423/fine-tune-test"
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SYSTEM = "You are a knowledgeable assistant for a solar energy company."
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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quantization_config=BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_compute_dtype=torch.float16,
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bnb_4bit_quant_type="nf4",
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),
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device_map="auto",
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)
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history = [{"role": "system", "content": SYSTEM}]
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while True:
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user = input("You: ").strip()
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if not user or user.lower() in {"exit", "quit"}:
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break
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history.append({"role": "user", "content": user})
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prompt = tokenizer.apply_chat_template(
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history, tokenize=False, add_generation_prompt=True
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)
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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with torch.no_grad():
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out = model.generate(
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**inputs, max_new_tokens=512,
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temperature=0.1, top_p=0.9, do_sample=True,
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pad_token_id=tokenizer.eos_token_id,
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)
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response = tokenizer.decode(
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out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True
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).strip()
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print(f"Assistant: {response}\n")
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history.append({"role": "assistant", "content": response})
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```
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---
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## Platform Support
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| Platform | Method | RAM / VRAM | Speed |
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|----------|--------|-----------|-------|
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| Mac M1/M2/M3/M4 | MLX (4-bit) | 5 GB | Fast |
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| NVIDIA GPU (Linux/Windows) | transformers 4-bit | 5–6 GB VRAM | Fast |
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| Google Colab T4 | transformers 4-bit | ~6 GB VRAM | Fast |
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| Kaggle P100 | transformers 4-bit | ~6 GB VRAM | Fast |
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| CPU — any OS | transformers float16 | 16 GB RAM | Slow |
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| **Any platform (recommended)** | **[GGUF 4.6 GB](https://huggingface.co/ankur1423/fine-tune-test-gguf)** | **6 GB RAM** | **Fast/OK** |
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> **Tip:** For CPU or low-VRAM machines, use the [GGUF version](https://huggingface.co/ankur1423/fine-tune-test-gguf) — same quality, 4.6 GB, no GPU needed.
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---
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## Recommended Generation Parameters
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| Parameter | Value | Notes |
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|-----------|-------|-------|
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| `temperature` | 0.1 | Low → factual, consistent |
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| `top_p` | 0.9 | Nucleus sampling |
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| `max_new_tokens` | 256–512 | FAQ answers are concise |
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| `do_sample` | True | Required when `temperature > 0` |
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Raise `temperature` to 0.5–0.7 for more varied / creative responses.
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---
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## Prompt Format
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This model uses the **Llama-3 chat template** with `<|eot_id|>` as the stop token.
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`tokenizer.apply_chat_template()` handles formatting automatically.
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Raw format:
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```
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<|begin_of_text|><|start_header_id|>system<|end_header_id|>
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You are a knowledgeable assistant for a solar energy company.<|eot_id|>
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<|start_header_id|>user<|end_header_id|>
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What is a BOM?<|eot_id|>
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<|start_header_id|>assistant<|end_header_id|>
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```
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Stop token: `<|eot_id|>`
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---
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## Training Details
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### Fine-tuning Process
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The model was fine-tuned using **LoRA (Low-Rank Adaptation)** — only a small set of adapter
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weights are trained; the base model weights are frozen. This allows high-quality fine-tuning
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with minimal compute and memory.
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| | |
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|---|---|
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| Base model | `meta-llama/Meta-Llama-3.1-8B-Instruct` |
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| Fine-tuning method | LoRA |
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| LoRA rank | 8 |
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| LoRA layers | 8 (attention layers) |
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| Dataset size | 62 train + 6 validation (68 total Q&A pairs) |
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| Iterations | 300 |
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| Learning rate | 1e-4 (cosine decay → 1e-5) |
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| Warmup steps | 30 |
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| Batch size | 2 |
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| Max sequence length | 1024 tokens |
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| Framework | [MLX-LM](https://github.com/ml-explore/mlx-examples) |
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| Training hardware | MacBook M4 16 GB unified memory |
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| Training time | ~20 minutes |
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### Dataset
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The training dataset consists of ~68 solar energy FAQ Q&A pairs covering topics such as:
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- Bill of Materials (BOM) and procurement
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- Production Planning & Control (PPC)
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- Solar panel manufacturing processes
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- Quality control and internal audits
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- Company operations and workflows
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Data format — Llama-3 chat template, one Q&A pair per record:
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```json
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{"text": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n[system]<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n[question]<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n[answer]<|eot_id|>"}
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```
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---
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## Ethics and Safety
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- Model is domain-specific and not a general-purpose assistant
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- Answers are based on training data — verify critical information independently
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- Not intended for medical, legal, or financial advice
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- Solar energy domain only — out-of-domain queries fall back to base Llama-3 behaviour
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- Inherits all safety characteristics of the base `meta-llama/Meta-Llama-3.1-8B-Instruct` model
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---
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## Usage and Limitations
|
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### Intended Use
|
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|
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- Solar energy company FAQ chatbot
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- Internal knowledge base assistant
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- Learning / research on domain-specific LoRA fine-tuning with MLX
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|
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### Out-of-Scope Use
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- General-purpose assistant (use base Llama-3.1 instead)
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- Medical, legal, or financial advice
|
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- Real-time data retrieval (model has no internet access)
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- Languages other than English
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### Known Limitations
|
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- Small dataset (~68 pairs) — may not generalize to all solar topics
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- English only
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- Float16 format requires ~15 GB disk and ~6 GB VRAM / 16 GB RAM
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- Apple Silicon only for MLX inference (use transformers on other platforms)
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|
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---
|
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## License
|
||||
|
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This model is derived from Meta Llama 3.1, which is licensed under the
|
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[Meta Llama 3 Community License](https://llama.meta.com/llama3/license/).
|
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Use is subject to Meta's acceptable use policy.
|
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109
chat_template.jinja
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109
chat_template.jinja
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{{- bos_token }}
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{%- if custom_tools is defined %}
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{%- set tools = custom_tools %}
|
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{%- endif %}
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{%- if not tools_in_user_message is defined %}
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{%- set tools_in_user_message = true %}
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{%- endif %}
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{%- if not date_string is defined %}
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{%- set date_string = "26 Jul 2024" %}
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{%- endif %}
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{%- if not tools is defined %}
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{%- set tools = none %}
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{%- endif %}
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{#- This block extracts the system message, so we can slot it into the right place. #}
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{%- if messages[0]['role'] == 'system' %}
|
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{%- set system_message = messages[0]['content']|trim %}
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{%- set messages = messages[1:] %}
|
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{%- else %}
|
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{%- set system_message = "" %}
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{%- endif %}
|
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|
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{#- System message + builtin tools #}
|
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{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
|
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{%- if builtin_tools is defined or tools is not none %}
|
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{{- "Environment: ipython\n" }}
|
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{%- endif %}
|
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{%- if builtin_tools is defined %}
|
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{{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "\n\n"}}
|
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{%- endif %}
|
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{{- "Cutting Knowledge Date: December 2023\n" }}
|
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{{- "Today Date: " + date_string + "\n\n" }}
|
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{%- if tools is not none and not tools_in_user_message %}
|
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{{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
|
||||
{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
|
||||
{{- "Do not use variables.\n\n" }}
|
||||
{%- for t in tools %}
|
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{{- t | tojson(indent=4) }}
|
||||
{{- "\n\n" }}
|
||||
{%- endfor %}
|
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{%- endif %}
|
||||
{{- system_message }}
|
||||
{{- "<|eot_id|>" }}
|
||||
|
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{#- Custom tools are passed in a user message with some extra guidance #}
|
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{%- if tools_in_user_message and not tools is none %}
|
||||
{#- Extract the first user message so we can plug it in here #}
|
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{%- if messages | length != 0 %}
|
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{%- set first_user_message = messages[0]['content']|trim %}
|
||||
{%- set messages = messages[1:] %}
|
||||
{%- else %}
|
||||
{{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
|
||||
{%- endif %}
|
||||
{{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
|
||||
{{- "Given the following functions, please respond with a JSON for a function call " }}
|
||||
{{- "with its proper arguments that best answers the given prompt.\n\n" }}
|
||||
{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
|
||||
{{- "Do not use variables.\n\n" }}
|
||||
{%- for t in tools %}
|
||||
{{- t | tojson(indent=4) }}
|
||||
{{- "\n\n" }}
|
||||
{%- endfor %}
|
||||
{{- first_user_message + "<|eot_id|>"}}
|
||||
{%- endif %}
|
||||
|
||||
{%- for message in messages %}
|
||||
{%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
|
||||
{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
|
||||
{%- elif 'tool_calls' in message %}
|
||||
{%- if not message.tool_calls|length == 1 %}
|
||||
{{- raise_exception("This model only supports single tool-calls at once!") }}
|
||||
{%- endif %}
|
||||
{%- set tool_call = message.tool_calls[0].function %}
|
||||
{%- if builtin_tools is defined and tool_call.name in builtin_tools %}
|
||||
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
|
||||
{{- "<|python_tag|>" + tool_call.name + ".call(" }}
|
||||
{%- for arg_name, arg_val in tool_call.arguments | items %}
|
||||
{{- arg_name + '="' + arg_val + '"' }}
|
||||
{%- if not loop.last %}
|
||||
{{- ", " }}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{{- ")" }}
|
||||
{%- else %}
|
||||
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
|
||||
{{- '{"name": "' + tool_call.name + '", ' }}
|
||||
{{- '"parameters": ' }}
|
||||
{{- tool_call.arguments | tojson }}
|
||||
{{- "}" }}
|
||||
{%- endif %}
|
||||
{%- if builtin_tools is defined %}
|
||||
{#- This means we're in ipython mode #}
|
||||
{{- "<|eom_id|>" }}
|
||||
{%- else %}
|
||||
{{- "<|eot_id|>" }}
|
||||
{%- endif %}
|
||||
{%- elif message.role == "tool" or message.role == "ipython" %}
|
||||
{{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
|
||||
{%- if message.content is mapping or message.content is iterable %}
|
||||
{{- message.content | tojson }}
|
||||
{%- else %}
|
||||
{{- message.content }}
|
||||
{%- endif %}
|
||||
{{- "<|eot_id|>" }}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
|
||||
{%- endif %}
|
||||
38
config.json
Normal file
38
config.json
Normal file
@@ -0,0 +1,38 @@
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{
|
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"architectures": [
|
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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128009
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
|
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": {
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"factor": 8.0,
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"low_freq_factor": 1.0,
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"high_freq_factor": 4.0,
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"original_max_position_embeddings": 8192,
|
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"rope_type": "llama3"
|
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},
|
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"rope_theta": 500000.0,
|
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"tie_word_embeddings": false,
|
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"torch_dtype": "bfloat16",
|
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"transformers_version": "4.42.3",
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"use_cache": true,
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"vocab_size": 128256
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3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
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||||
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14
tokenizer_config.json
Normal file
14
tokenizer_config.json
Normal file
@@ -0,0 +1,14 @@
|
||||
{
|
||||
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|
||||
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|
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||||
}
|
||||
Reference in New Issue
Block a user