finance chat GGUF
GGUF quantizations of AdaptLLM/finance-chat, verified end-to-end on the NVIDIA DGX Spark (GB10, 128 GB unified memory).
Notebooks
Two runnable notebooks ship with this model — open either on a free cloud GPU:
| Notebook | What it does | Open |
|---|---|---|
| Builder | Reproduce this model's build and DGX Spark benchmarks end-to-end with fieldkit. |
👁 Open In Colab 👁 Open in Kaggle |
| User | Load the published model and call it from your own app in a few lines. | 👁 Open In Colab 👁 Open in Kaggle |
Spark-tested
Every Orionfold quant ships with a measurement quad on the NVIDIA DGX Spark (GB10, 128 GB unified memory): perplexity, sustained tok/s, thermal envelope, and FinanceBench (n=50, numeric_match) accuracy. The numbers below are the actual run, not a wishlist.
| Variant | Size | Perplexity (wikitext-2) | tok/s on Spark | FinanceBench (n=50, numeric_match) |
|---|---|---|---|---|
| Q4_K_M | 3.8 GB | 6.221 | 31.1 | 14.0% |
| Q5_K_M | 4.5 GB | 6.164 | 26.9 | 16.0% |
| Q6_K | 5.1 GB | 6.147 | 23.9 | 16.0% |
| Q8_0 | 6.7 GB | 6.137 | 8.9 | 18.0% |
| F16 | 12.6 GB | 6.137 | 11.5 | 18.0% |
Thermal envelope: sustained-load minutes before thermal throttle on a single GB10 = 2 min. Beyond this, expect tok/s degradation; the duty-cycle disclosure is per Orionfold's quant-card standard.
Variants
| Variant | Recommended use |
|---|---|
| Q4_K_M | Best balance — fits comfortably in Spark unified memory at 70B; default pick. |
| Q5_K_M | Higher quality than Q4_K_M with modest size bump. |
| Q6_K | Near-lossless; recommended if memory headroom allows. |
| Q8_0 | Effectively lossless; reach for this when quality matters more than throughput. |
| F16 | Reference — no quantization. Use only for measurement / baseline. |
How to run
Pull a variant:
huggingface-cli download Orionfold/finance-chat-GGUF model-Q5_K_M.gguf \
--local-dir ./models/finance-chat
Serve it via llama-server (OpenAI-compatible API):
llama-server -m ./models/finance-chat/model-Q5_K_M.gguf \
-c 4096 -ngl 99 -t 8 \
--host 0.0.0.0 --port 8080
Or run in-process via llama-cpp-python:
from llama_cpp import Llama
llm = Llama(
model_path="./models/finance-chat/model-Q5_K_M.gguf",
n_ctx=4096, n_gpu_layers=99, chat_format="llama-2",
)
out = llm.create_chat_completion(
messages=[{"role": "user", "content": "Explain working capital."}],
temperature=0.0,
)
print(out["choices"][0]["message"]["content"])
LM Studio and Ollama (via a Modelfile) load the GGUF directly with no additional setup.
Methods
Full methodology and Spark-side measurement protocol: Vertical-curator quants on Spark — finance-chat-GGUF + FinanceBench mini-eval.
Other Orionfold vertical curators
Same Spark-tested recipe across the curator-on-Spark series:
- Saul-7B-Instruct-v1-GGUF — Equall Saul-7B legal-instruct for LegalBench-shaped queries
- SecurityLLM-GGUF — Mistral-based cyber-tuned model with CyberMetric mini-eval gating
- II-Medical-8B-GGUF — Qwen3-8B + DAPO reasoning for MedMCQA-shaped queries
Each card lists its own measurement quad; the headline numbers are recorded as the actual sweep ran, never pre-corrected.
Published by Orionfold LLC · orionfold.com · Methods documented at ainative.business/field-notes.
Want to know when the next Orionfold vertical curator drops? Join the launch list at orionfold.com.
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Model tree for Orionfold/finance-chat-GGUF
Base model
AdaptLLM/finance-chat