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Model: ankur1423/fine-tune-test
Source: Original Platform

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.placeholder

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+fine-tune-test/ created. Will be populated by convert_to_hf.py after training.

README.md

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+---
+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.

chat_template.jinja

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+{{- 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 %}

config.json

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+{
+    "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",
+    "use_cache": true,
+    "vocab_size": 128256
+}

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tokenizer_config.json

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