commit ddadd9e8472674df105c561b2dd593fbd2797a94 Author: ModelHub XC Date: Sun Jun 21 08:56:16 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: ankur1423/fine-tune-test Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..52373fe --- /dev/null +++ b/.gitattributes @@ -0,0 +1,36 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text diff --git a/.placeholder b/.placeholder new file mode 100644 index 0000000..fe9996c --- /dev/null +++ b/.placeholder @@ -0,0 +1 @@ +fine-tune-test/ created. Will be populated by convert_to_hf.py after training. diff --git a/README.md b/README.md new file mode 100644 index 0000000..b15f5a7 --- /dev/null +++ b/README.md @@ -0,0 +1,346 @@ +--- +language: +- en +license: llama3 +license_link: https://llama.meta.com/llama3/license/ +library_name: transformers +base_model: meta-llama/Meta-Llama-3.1-8B-Instruct +tags: +- llama-3 +- lora +- fine-tuned +- solar-energy +- text-generation +- mlx +- apple-silicon +pipeline_tag: text-generation +--- + +# Solar FAQ — Llama-3.1-8B LoRA Fine-tune + +A **Llama-3.1-8B-Instruct** model fine-tuned with LoRA on a solar energy FAQ dataset +using [MLX-LM](https://github.com/ml-explore/mlx-examples/tree/main/llms) on Apple Silicon. + +| | | +|---|---| +| Base model | `meta-llama/Meta-Llama-3.1-8B-Instruct` | +| Format | float16 safetensors (safe — no pickle) | +| Size | ~15 GB (float16) | +| Fine-tune method | LoRA rank 8, 8 layers | +| Domain | Solar energy FAQ | +| Languages | English | + +> **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. + +--- + +## Model Overview + +This model is a LoRA fine-tune experiment on top of Meta's Llama-3.1-8B-Instruct, +trained on a small domain-specific solar energy FAQ dataset (~62 Q&A pairs). +It answers questions about solar products, manufacturing processes, and company operations. + +Outside the training domain it falls back to standard Llama-3.1 behaviour. + +### What it can do + +- Answer solar energy FAQ questions accurately +- Explain solar manufacturing concepts (BOM, PPC, audits, etc.) +- Provide concise, professional responses to domain-specific queries +- Multi-turn conversation with context retention + +### What it cannot do + +- General-purpose assistant (use base Llama-3.1 for that) +- Image / audio / video understanding +- Real-time or internet-connected queries + +--- + +## Getting Started + +### Installation + +```bash +# GPU (NVIDIA) or CPU: +pip install transformers torch accelerate bitsandbytes + +# Apple Silicon (recommended — faster with MLX): +pip install mlx-lm +``` + +### Quick Inference (transformers) + +```python +from transformers import AutoModelForCausalLM, AutoTokenizer +import torch + +model_id = "ankur1423/fine-tune-test" + +tokenizer = AutoTokenizer.from_pretrained(model_id) +model = AutoModelForCausalLM.from_pretrained( + model_id, + torch_dtype=torch.float16, + device_map="auto", # auto: GPU if available, else CPU +) + +def ask(question: str) -> str: + messages = [ + {"role": "system", + "content": "You are a knowledgeable assistant for a solar energy company. " + "Answer questions accurately about solar products, manufacturing, and company operations."}, + {"role": "user", "content": question}, + ] + prompt = tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True, + ) + inputs = tokenizer(prompt, return_tensors="pt").to(model.device) + with torch.no_grad(): + output = model.generate( + **inputs, + max_new_tokens=512, + temperature=0.1, + top_p=0.9, + do_sample=True, + pad_token_id=tokenizer.eos_token_id, + ) + new_tokens = output[0][inputs["input_ids"].shape[1]:] + return tokenizer.decode(new_tokens, skip_special_tokens=True).strip() + +print(ask("What is a BOM?")) +print(ask("What is PPC in solar manufacturing?")) +print(ask("Why are internal audits important?")) +``` + +### 4-bit Quantized Inference (saves ~12 GB RAM) + +```python +from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig +import torch + +model_id = "ankur1423/fine-tune-test" + +bnb_config = BitsAndBytesConfig( + load_in_4bit=True, + bnb_4bit_compute_dtype=torch.float16, + bnb_4bit_use_double_quant=True, + bnb_4bit_quant_type="nf4", +) + +tokenizer = AutoTokenizer.from_pretrained(model_id) +model = AutoModelForCausalLM.from_pretrained( + model_id, + quantization_config=bnb_config, + device_map="auto", +) + +# Same ask() function as above — uses ~5 GB VRAM instead of 15 GB +``` + +### Apple Silicon — MLX (fastest on Mac) + +```python +from mlx_lm import load, generate +from mlx_lm.generate import make_sampler + +model, tokenizer = load("ankur1423/fine-tune-test") + +SYSTEM = "You are a knowledgeable assistant for a solar energy company." + +def ask(question: str) -> str: + prompt = ( + "<|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" + ) + return generate( + model, tokenizer, + prompt=prompt, + max_tokens=512, + sampler=make_sampler(temp=0.1, top_p=0.9), + ) + +print(ask("What is a BOM?")) +``` + +### Multi-turn Chat (transformers) + +```python +from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig +import torch + +model_id = "ankur1423/fine-tune-test" +SYSTEM = "You are a knowledgeable assistant for a solar energy company." + +tokenizer = AutoTokenizer.from_pretrained(model_id) +model = AutoModelForCausalLM.from_pretrained( + model_id, + quantization_config=BitsAndBytesConfig( + load_in_4bit=True, + bnb_4bit_compute_dtype=torch.float16, + bnb_4bit_quant_type="nf4", + ), + device_map="auto", +) + +history = [{"role": "system", "content": SYSTEM}] + +while True: + user = input("You: ").strip() + if not user or user.lower() in {"exit", "quit"}: + break + history.append({"role": "user", "content": user}) + + prompt = tokenizer.apply_chat_template( + history, tokenize=False, add_generation_prompt=True + ) + inputs = tokenizer(prompt, return_tensors="pt").to(model.device) + with torch.no_grad(): + out = model.generate( + **inputs, max_new_tokens=512, + temperature=0.1, top_p=0.9, do_sample=True, + pad_token_id=tokenizer.eos_token_id, + ) + response = tokenizer.decode( + out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True + ).strip() + print(f"Assistant: {response}\n") + history.append({"role": "assistant", "content": response}) +``` + +--- + +## Platform Support + +| Platform | Method | RAM / VRAM | Speed | +|----------|--------|-----------|-------| +| Mac M1/M2/M3/M4 | MLX (4-bit) | 5 GB | Fast | +| NVIDIA GPU (Linux/Windows) | transformers 4-bit | 5–6 GB VRAM | Fast | +| Google Colab T4 | transformers 4-bit | ~6 GB VRAM | Fast | +| Kaggle P100 | transformers 4-bit | ~6 GB VRAM | Fast | +| CPU — any OS | transformers float16 | 16 GB RAM | Slow | +| **Any platform (recommended)** | **[GGUF 4.6 GB](https://huggingface.co/ankur1423/fine-tune-test-gguf)** | **6 GB RAM** | **Fast/OK** | + +> **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. + +--- + +## Recommended Generation Parameters + +| Parameter | Value | Notes | +|-----------|-------|-------| +| `temperature` | 0.1 | Low → factual, consistent | +| `top_p` | 0.9 | Nucleus sampling | +| `max_new_tokens` | 256–512 | FAQ answers are concise | +| `do_sample` | True | Required when `temperature > 0` | + +Raise `temperature` to 0.5–0.7 for more varied / creative responses. + +--- + +## Prompt Format + +This model uses the **Llama-3 chat template** with `<|eot_id|>` as the stop token. + +`tokenizer.apply_chat_template()` handles formatting automatically. + +Raw format: +``` +<|begin_of_text|><|start_header_id|>system<|end_header_id|> + +You are a knowledgeable assistant for a solar energy company.<|eot_id|> +<|start_header_id|>user<|end_header_id|> + +What is a BOM?<|eot_id|> +<|start_header_id|>assistant<|end_header_id|> + +``` + +Stop token: `<|eot_id|>` + +--- + +## Training Details + +### Fine-tuning Process + +The model was fine-tuned using **LoRA (Low-Rank Adaptation)** — only a small set of adapter +weights are trained; the base model weights are frozen. This allows high-quality fine-tuning +with minimal compute and memory. + +| | | +|---|---| +| Base model | `meta-llama/Meta-Llama-3.1-8B-Instruct` | +| Fine-tuning method | LoRA | +| LoRA rank | 8 | +| LoRA layers | 8 (attention layers) | +| Dataset size | 62 train + 6 validation (68 total Q&A pairs) | +| Iterations | 300 | +| Learning rate | 1e-4 (cosine decay → 1e-5) | +| Warmup steps | 30 | +| Batch size | 2 | +| Max sequence length | 1024 tokens | +| Framework | [MLX-LM](https://github.com/ml-explore/mlx-examples) | +| Training hardware | MacBook M4 16 GB unified memory | +| Training time | ~20 minutes | + +### Dataset + +The training dataset consists of ~68 solar energy FAQ Q&A pairs covering topics such as: +- Bill of Materials (BOM) and procurement +- Production Planning & Control (PPC) +- Solar panel manufacturing processes +- Quality control and internal audits +- Company operations and workflows + +Data format — Llama-3 chat template, one Q&A pair per record: +```json +{"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|>"} +``` + +--- + +## Ethics and Safety + +- Model is domain-specific and not a general-purpose assistant +- Answers are based on training data — verify critical information independently +- Not intended for medical, legal, or financial advice +- Solar energy domain only — out-of-domain queries fall back to base Llama-3 behaviour +- Inherits all safety characteristics of the base `meta-llama/Meta-Llama-3.1-8B-Instruct` model + +--- + +## Usage and Limitations + +### Intended Use + +- Solar energy company FAQ chatbot +- Internal knowledge base assistant +- Learning / research on domain-specific LoRA fine-tuning with MLX + +### Out-of-Scope Use + +- General-purpose assistant (use base Llama-3.1 instead) +- Medical, legal, or financial advice +- Real-time data retrieval (model has no internet access) +- Languages other than English + +### Known Limitations + +- Small dataset (~68 pairs) — may not generalize to all solar topics +- English only +- Float16 format requires ~15 GB disk and ~6 GB VRAM / 16 GB RAM +- Apple Silicon only for MLX inference (use transformers on other platforms) + +--- + +## License + +This model is derived from Meta Llama 3.1, which is licensed under the +[Meta Llama 3 Community License](https://llama.meta.com/llama3/license/). +Use is subject to Meta's acceptable use policy. diff --git a/chat_template.jinja b/chat_template.jinja new file mode 100644 index 0000000..33089ac --- /dev/null +++ b/chat_template.jinja @@ -0,0 +1,109 @@ +{{- bos_token }} +{%- if custom_tools is defined %} + {%- set tools = custom_tools %} +{%- endif %} +{%- if not tools_in_user_message is defined %} + {%- set tools_in_user_message = true %} +{%- endif %} +{%- if not date_string is defined %} + {%- set date_string = "26 Jul 2024" %} +{%- endif %} +{%- if not tools is defined %} + {%- set tools = none %} +{%- endif %} + +{#- This block extracts the system message, so we can slot it into the right place. #} +{%- if messages[0]['role'] == 'system' %} + {%- set system_message = messages[0]['content']|trim %} + {%- set messages = messages[1:] %} +{%- else %} + {%- set system_message = "" %} +{%- endif %} + +{#- System message + builtin tools #} +{{- "<|start_header_id|>system<|end_header_id|>\n\n" }} +{%- if builtin_tools is defined or tools is not none %} + {{- "Environment: ipython\n" }} +{%- endif %} +{%- if builtin_tools is defined %} + {{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "\n\n"}} +{%- endif %} +{{- "Cutting Knowledge Date: December 2023\n" }} +{{- "Today Date: " + date_string + "\n\n" }} +{%- if tools is not none and not tools_in_user_message %} + {{- "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 %} + {{- t | tojson(indent=4) }} + {{- "\n\n" }} + {%- endfor %} +{%- endif %} +{{- system_message }} +{{- "<|eot_id|>" }} + +{#- Custom tools are passed in a user message with some extra guidance #} +{%- if tools_in_user_message and not tools is none %} + {#- Extract the first user message so we can plug it in here #} + {%- if messages | length != 0 %} + {%- 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 %} diff --git a/config.json b/config.json new file mode 100644 index 0000000..a3bbba3 --- /dev/null +++ b/config.json @@ -0,0 +1,38 @@ +{ + "architectures": [ + "LlamaForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 128000, + "eos_token_id": [ + 128001, + 128008, + 128009 + ], + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 131072, + "mlp_bias": false, + "model_type": "llama", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pretraining_tp": 1, + "rms_norm_eps": 1e-05, + "rope_scaling": { + "factor": 8.0, + "low_freq_factor": 1.0, + "high_freq_factor": 4.0, + "original_max_position_embeddings": 8192, + "rope_type": "llama3" + }, + "rope_theta": 500000.0, + "tie_word_embeddings": false, + "torch_dtype": "bfloat16", + "transformers_version": "4.42.3", + 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