Text Generation
PEFT
Safetensors
English
lora
molly-os
specialist
climate-analytics-manager
llama-3.1
domain-adaptation
Instructions to use BoomJules/molly-climate-analytics-manager with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use BoomJules/molly-climate-analytics-manager with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B-Instruct") model = PeftModel.from_pretrained(base_model, "BoomJules/molly-climate-analytics-manager") - Notebooks
- Google Colab
- Kaggle
| library_name: peft | |
| base_model: meta-llama/Llama-3.1-8B-Instruct | |
| pipeline_tag: text-generation | |
| language: | |
| - en | |
| license: cc-by-nc-4.0 | |
| tags: | |
| - lora | |
| - peft | |
| - molly-os | |
| - specialist | |
| - climate-analytics-manager | |
| - llama-3.1 | |
| - domain-adaptation | |
| # Molly Specialist β Climate Analytics Manager | |
| Quantifies Scope 1-3 emissions across portfolios, benchmarks against SBTi pathways, and translates climate scenarios into financial risk metrics more accurately than the base model. | |
| Part of **[Molly](https://iamolly.ai/?utm_source=huggingface&utm_medium=model_card&utm_campaign=specialists&utm_content=molly-climate-analytics-manager)**, an orchestrator that keeps a library of small domain | |
| specialists over one quantized base and routes each request to the right one, so a | |
| single machine answers across many fields without loading a separate large model | |
| for each. | |
| ## What this specialist handles well | |
| - Calculates financed emissions using PCAF methodology across asset classes | |
| - Maps physical climate hazards to asset-level financial exposure with TCFD alignment | |
| - Compares transition scenarios from NGFS and IEA for portfolio stress testing | |
| ## Try it with | |
| - "What are the PCAF emission factors for corporate equity holdings in 2026?" | |
| - "How do NGFS Net Zero 2050 scenario assumptions affect our oil and gas portfolio?" | |
| - "Which assets in our real estate portfolio face highest acute physical risk by 2030?" | |
| ## Before you run: the base model is gated | |
| This adapter needs the base weights, and the base is **access-gated**. Do this **once**: | |
| 1. Accept the base licence: <https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct> | |
| 2. Create a **read token**: <https://huggingface.co/settings/tokens> | |
| 3. Make the token available: | |
| - **Google Colab:** Secrets panel (key icon) β *Add new secret* β name `HF_TOKEN`, enable **Notebook access**. | |
| - **Kaggle:** *Add-ons β Secrets* β add `HF_TOKEN`. | |
| - **Local:** `huggingface-cli login` or `export HF_TOKEN=...` | |
| Skipping this gives `GatedRepoError` / `401 Unauthorized` when the **base** loads. A stored | |
| Colab secret is **not** applied automatically β authenticate in code, as below. | |
| ## Quickstart | |
| ```python | |
| # pip install -U transformers peft accelerate | |
| import os, torch | |
| from huggingface_hub import login | |
| try: | |
| from google.colab import userdata | |
| login(userdata.get("HF_TOKEN")) | |
| except Exception: | |
| tok = os.environ.get("HF_TOKEN") | |
| login(tok) if tok else login() | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| from peft import PeftModel | |
| BASE = "meta-llama/Llama-3.1-8B-Instruct" | |
| ADAPTER = "BoomJules/molly-climate-analytics-manager" | |
| tok = AutoTokenizer.from_pretrained(BASE) | |
| base = AutoModelForCausalLM.from_pretrained(BASE, torch_dtype=torch.bfloat16, device_map="auto") | |
| model = PeftModel.from_pretrained(base, ADAPTER).eval() | |
| msgs = [{"role": "user", "content": "Your question here"}] | |
| ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device) | |
| out = model.generate(ids, max_new_tokens=300) | |
| print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True)) | |
| ``` | |
| ## Low-VRAM (4-bit) β fits a free Colab/Kaggle GPU (~6β7 GB) | |
| ```python | |
| # pip install -U transformers peft accelerate bitsandbytes | |
| import os, torch | |
| from huggingface_hub import login | |
| try: | |
| from google.colab import userdata | |
| login(userdata.get("HF_TOKEN")) | |
| except Exception: | |
| login(os.environ.get("HF_TOKEN")) | |
| from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig | |
| from peft import PeftModel | |
| bnb = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4", | |
| bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True) | |
| tok = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct") | |
| base = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B-Instruct", quantization_config=bnb, device_map="auto") | |
| model = PeftModel.from_pretrained(base, "BoomJules/molly-climate-analytics-manager").eval() | |
| ``` | |
| ## Adapter details | |
| | | | | |
| |---|---| | |
| | Base model | `meta-llama/Llama-3.1-8B-Instruct` | | |
| | Method | LoRA (PEFT) | | |
| | Rank / alpha | 32 / 64 | | |
| | Domain | Climate Analytics Manager | | |
| ## Troubleshooting | |
| - **`GatedRepoError` / `401 Unauthorized`** β base licence not accepted, or `HF_TOKEN` missing, | |
| or the Colab secret was stored but `login(...)` was never called. | |
| - **CUDA out of memory** β use the 4-bit snippet on a GPU runtime. | |
| - **Adapter seems to have no effect** β confirm the base id matches `base_model` above. | |
| ## Other Molly specialists | |
| - [Quantum Software Architect](https://huggingface.co/BoomJules/molly-quantum-software-architect) | |
| - [Quantum Communication Systems Engineer](https://huggingface.co/BoomJules/molly-quantum-communication-systems-engineer) | |
| - [Infectious Disease Physician Antimicrobial Stewardship](https://huggingface.co/BoomJules/molly-infectious-disease-physician-antimicrobial-stewardship) | |
| - [Health Informatics Medical AI Specialist](https://huggingface.co/BoomJules/molly-health-informatics-medical-ai-specialist) | |
| - [Clinical Trial Pharmacologist](https://huggingface.co/BoomJules/molly-clinical-trial-pharmacologist) | |
| - [Immunopharmacologist](https://huggingface.co/BoomJules/molly-immunopharmacologist) | |
| - [Language Technology Consultant](https://huggingface.co/BoomJules/molly-language-technology-consultant) | |
| - [Polymer Chemist](https://huggingface.co/BoomJules/molly-polymer-chemist) | |
| - [Composite Materials Engineer](https://huggingface.co/BoomJules/molly-composite-materials-engineer) | |
| - [Computer Science AI](https://huggingface.co/BoomJules/molly-cs-ai) | |
| - [Computer Science Algorithms](https://huggingface.co/BoomJules/molly-cs-algorithms) | |
| - [Computer Science Computer Vision](https://huggingface.co/BoomJules/molly-cs-cv) | |
| Running several of these at once, with the routing decided for you, is what | |
| [Molly](https://iamolly.ai/?utm_source=huggingface&utm_medium=model_card&utm_campaign=specialists&utm_content=molly-climate-analytics-manager) does. | |
| ## Licence & intended use | |
| Adapter: **CC BY-NC 4.0** (attribution, non-commercial). Base model: its own licence. | |
| Intended for research and evaluation in Climate Analytics Manager. | |
| Β© 2026 Core Labs R&D. | |